[2024-10-21 11:18:21] Experiment directory created at ./results_long_all_64/053-UNet-vimeoshot-Gc-320_512
[2024-10-21 11:18:21] Load ./large-video-v2/results_long_all_64/052-UNet-vimeoshot-Gc-320_512/checkpoints/0120000.pt as pretrained model
[2024-10-21 11:20:00] Using ema ckpt!
[2024-10-21 11:20:00] Successfully Load 100.0% original pretrained model weights!
[2024-10-21 11:20:01] Successfully load model at ./large-video-v2/results_long_all_64/052-UNet-vimeoshot-Gc-320_512/checkpoints/0120000.pt!
[2024-10-21 11:20:01] Using gradient checkpointing!
[2024-10-21 11:20:03] Model Parameters: 909,124,100
[2024-10-21 11:20:03] Loading pretrained stable diffusion models!
[2024-10-21 11:20:05] Loaded optimizer from ./large-video-v2/results_long_all_64/052-UNet-vimeoshot-Gc-320_512/checkpoints/0120000.pt!
[2024-10-21 11:20:22] Dataset contains: 251,881
[2024-10-21 11:20:22] Total train batch size (w. parallel, distributed & accumulation) = 128
[2024-10-21 11:20:22] Using 0 images per video!
[2024-10-21 11:20:22] Loaded lr_scheduler from ./large-video-v2/results_long_all_64/052-UNet-vimeoshot-Gc-320_512/checkpoints/0120000.pt!
[2024-10-21 11:21:23] Begin training!
[2024-10-21 11:24:08] (step=0120020/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0034, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-21 11:27:00] (step=0120040/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:29:52] (step=0120060/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:32:45] (step=0120080/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:35:37] (step=0120100/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:38:29] (step=0120120/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:41:23] (step=0120140/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:44:16] (step=0120160/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:47:09] (step=0120180/epoch=0007) Train Loss: 0.0068, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:50:01] (step=0120200/epoch=0007) Train Loss: 0.0108, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 11:53:03] (step=0120220/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 11:56:03] (step=0120240/epoch=0007) Train Loss: 0.0072, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 11:58:56] (step=0120260/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:01:48] (step=0120280/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:05:47] (step=0120300/epoch=0007) Train Loss: 0.0072, Gradient Norm: 0.0026, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-21 12:08:42] (step=0120320/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 12:11:35] (step=0120340/epoch=0007) Train Loss: 0.0068, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:14:32] (step=0120360/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 12:17:25] (step=0120380/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:20:18] (step=0120400/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:23:11] (step=0120420/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:26:03] (step=0120440/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:28:56] (step=0120460/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:31:48] (step=0120480/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:34:40] (step=0120500/epoch=0007) Train Loss: 0.0071, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:37:33] (step=0120520/epoch=0007) Train Loss: 0.0098, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:40:25] (step=0120540/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:43:21] (step=0120560/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 12:46:14] (step=0120580/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0018, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:49:06] (step=0120600/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 12:52:27] (step=0120620/epoch=0007) Train Loss: 0.0093, Gradient Norm: 0.0032, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-21 12:59:47] (step=0120640/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0060, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-21 13:03:11] (step=0120660/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0028, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-21 13:06:03] (step=0120680/epoch=0007) Train Loss: 0.0112, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:08:55] (step=0120700/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:11:48] (step=0120720/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:14:42] (step=0120740/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:17:34] (step=0120760/epoch=0007) Train Loss: 0.0070, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:20:28] (step=0120780/epoch=0007) Train Loss: 0.0102, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 13:23:20] (step=0120800/epoch=0007) Train Loss: 0.0097, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:26:13] (step=0120820/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:29:06] (step=0120840/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:31:58] (step=0120860/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:34:51] (step=0120880/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:37:46] (step=0120900/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 13:40:40] (step=0120920/epoch=0007) Train Loss: 0.0117, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 13:43:32] (step=0120940/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:46:26] (step=0120960/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:49:22] (step=0120980/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 13:52:14] (step=0121000/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:55:08] (step=0121020/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 13:58:01] (step=0121040/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:00:54] (step=0121060/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:03:47] (step=0121080/epoch=0007) Train Loss: 0.0102, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:06:39] (step=0121100/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:09:31] (step=0121120/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:12:24] (step=0121140/epoch=0007) Train Loss: 0.0099, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:15:17] (step=0121160/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:18:12] (step=0121180/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 14:21:04] (step=0121200/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:23:57] (step=0121220/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:26:50] (step=0121240/epoch=0007) Train Loss: 0.0105, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:30:05] (step=0121260/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0032, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-21 14:32:58] (step=0121280/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:35:51] (step=0121300/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:38:44] (step=0121320/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:41:37] (step=0121340/epoch=0007) Train Loss: 0.0096, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:44:31] (step=0121360/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:47:24] (step=0121380/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:50:17] (step=0121400/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:53:10] (step=0121420/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:56:02] (step=0121440/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 14:58:56] (step=0121460/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:01:48] (step=0121480/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:05:15] (step=0121500/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0022, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-21 15:08:08] (step=0121520/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:11:00] (step=0121540/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0016, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:14:05] (step=0121560/epoch=0007) Train Loss: 0.0096, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 15:17:02] (step=0121580/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 15:19:55] (step=0121600/epoch=0007) Train Loss: 0.0068, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:22:48] (step=0121620/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:25:41] (step=0121640/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:28:35] (step=0121660/epoch=0007) Train Loss: 0.0075, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 15:31:28] (step=0121680/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:34:20] (step=0121700/epoch=0007) Train Loss: 0.0073, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:37:13] (step=0121720/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:40:06] (step=0121740/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:42:59] (step=0121760/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:45:52] (step=0121780/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:48:44] (step=0121800/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:51:37] (step=0121820/epoch=0007) Train Loss: 0.0070, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 15:54:31] (step=0121840/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 15:57:36] (step=0121860/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 16:00:29] (step=0121880/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:03:23] (step=0121900/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:06:15] (step=0121920/epoch=0007) Train Loss: 0.0075, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:09:08] (step=0121940/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:12:01] (step=0121960/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:14:53] (step=0121980/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:17:47] (step=0122000/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:21:39] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0122000.pt
[2024-10-21 16:24:31] (step=0122020/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0035, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-21 16:27:25] (step=0122040/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:30:18] (step=0122060/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:33:11] (step=0122080/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:36:05] (step=0122100/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:38:57] (step=0122120/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:41:50] (step=0122140/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:44:42] (step=0122160/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:47:34] (step=0122180/epoch=0007) Train Loss: 0.0073, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:50:59] (step=0122200/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0090, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-21 16:53:52] (step=0122220/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 16:56:55] (step=0122240/epoch=0007) Train Loss: 0.0075, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 16:59:49] (step=0122260/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 17:02:42] (step=0122280/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:05:34] (step=0122300/epoch=0007) Train Loss: 0.0075, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:08:27] (step=0122320/epoch=0007) Train Loss: 0.0096, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:11:19] (step=0122340/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:14:13] (step=0122360/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:17:06] (step=0122380/epoch=0007) Train Loss: 0.0101, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:22:16] (step=0122400/epoch=0007) Train Loss: 0.0110, Gradient Norm: 0.0067, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-21 17:25:09] (step=0122420/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:28:01] (step=0122440/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:30:56] (step=0122460/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 17:34:06] (step=0122480/epoch=0007) Train Loss: 0.0071, Gradient Norm: 0.0048, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-21 17:37:00] (step=0122500/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:39:52] (step=0122520/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 17:42:51] (step=0122540/epoch=0007) Train Loss: 0.0073, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 17:47:18] (step=0122560/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0068, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-21 17:52:43] (step=0122580/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0033, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-21 17:55:47] (step=0122600/epoch=0007) Train Loss: 0.0103, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 17:58:40] (step=0122620/epoch=0007) Train Loss: 0.0115, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:01:34] (step=0122640/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:04:25] (step=0122660/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:07:19] (step=0122680/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:10:13] (step=0122700/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 18:13:06] (step=0122720/epoch=0007) Train Loss: 0.0093, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:16:10] (step=0122740/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 18:19:04] (step=0122760/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0110, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 18:21:58] (step=0122780/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 18:24:51] (step=0122800/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:27:45] (step=0122820/epoch=0007) Train Loss: 0.0069, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 18:30:38] (step=0122840/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:33:32] (step=0122860/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:36:23] (step=0122880/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:39:16] (step=0122900/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:42:08] (step=0122920/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:45:01] (step=0122940/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:47:53] (step=0122960/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:50:46] (step=0122980/epoch=0007) Train Loss: 0.0071, Gradient Norm: 0.0016, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:53:39] (step=0123000/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:56:32] (step=0123020/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 18:59:25] (step=0123040/epoch=0007) Train Loss: 0.0097, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:02:17] (step=0123060/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:05:10] (step=0123080/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:08:03] (step=0123100/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:10:57] (step=0123120/epoch=0007) Train Loss: 0.0067, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:13:52] (step=0123140/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 19:16:45] (step=0123160/epoch=0007) Train Loss: 0.0071, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:19:38] (step=0123180/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:22:32] (step=0123200/epoch=0007) Train Loss: 0.0099, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:25:25] (step=0123220/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:28:18] (step=0123240/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:31:11] (step=0123260/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:34:06] (step=0123280/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 19:36:59] (step=0123300/epoch=0007) Train Loss: 0.0098, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:39:52] (step=0123320/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:42:45] (step=0123340/epoch=0007) Train Loss: 0.0093, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:45:39] (step=0123360/epoch=0007) Train Loss: 0.0096, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 19:48:32] (step=0123380/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:51:26] (step=0123400/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 19:54:20] (step=0123420/epoch=0007) Train Loss: 0.0068, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 19:57:14] (step=0123440/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 20:00:07] (step=0123460/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:03:00] (step=0123480/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:05:53] (step=0123500/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:08:47] (step=0123520/epoch=0007) Train Loss: 0.0073, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:11:40] (step=0123540/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:14:33] (step=0123560/epoch=0007) Train Loss: 0.0107, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:17:26] (step=0123580/epoch=0007) Train Loss: 0.0099, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:20:19] (step=0123600/epoch=0007) Train Loss: 0.0109, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:23:13] (step=0123620/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:26:06] (step=0123640/epoch=0007) Train Loss: 0.0076, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:28:59] (step=0123660/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:31:52] (step=0123680/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:34:44] (step=0123700/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:37:37] (step=0123720/epoch=0007) Train Loss: 0.0093, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:40:30] (step=0123740/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:43:23] (step=0123760/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0108, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:46:16] (step=0123780/epoch=0007) Train Loss: 0.0098, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:49:09] (step=0123800/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:52:02] (step=0123820/epoch=0007) Train Loss: 0.0097, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:54:55] (step=0123840/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 20:57:48] (step=0123860/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:00:41] (step=0123880/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:03:37] (step=0123900/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 21:06:39] (step=0123920/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 21:09:38] (step=0123940/epoch=0007) Train Loss: 0.0072, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 21:12:31] (step=0123960/epoch=0007) Train Loss: 0.0075, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:15:24] (step=0123980/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:18:18] (step=0124000/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:22:09] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0124000.pt
[2024-10-21 21:25:01] (step=0124020/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0033, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-21 21:27:55] (step=0124040/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:30:48] (step=0124060/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 21:33:41] (step=0124080/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:36:34] (step=0124100/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:39:27] (step=0124120/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:42:20] (step=0124140/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:45:13] (step=0124160/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:48:06] (step=0124180/epoch=0007) Train Loss: 0.0103, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:51:00] (step=0124200/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:53:52] (step=0124220/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:56:46] (step=0124240/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 21:59:45] (step=0124260/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 22:02:37] (step=0124280/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:05:30] (step=0124300/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:08:23] (step=0124320/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:11:16] (step=0124340/epoch=0007) Train Loss: 0.0108, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:14:08] (step=0124360/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:17:01] (step=0124380/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:19:54] (step=0124400/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:22:48] (step=0124420/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:25:42] (step=0124440/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 22:28:35] (step=0124460/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:31:29] (step=0124480/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:34:21] (step=0124500/epoch=0007) Train Loss: 0.0093, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:39:04] (step=0124520/epoch=0007) Train Loss: 0.0099, Gradient Norm: 0.0074, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-21 22:45:38] (step=0124540/epoch=0007) Train Loss: 0.0073, Gradient Norm: 0.0026, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-21 22:48:31] (step=0124560/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:51:24] (step=0124580/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:54:17] (step=0124600/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 22:57:10] (step=0124620/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:00:06] (step=0124640/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 23:02:59] (step=0124660/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:05:52] (step=0124680/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:08:46] (step=0124700/epoch=0007) Train Loss: 0.0090, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:11:40] (step=0124720/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 23:14:33] (step=0124740/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:17:26] (step=0124760/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:20:19] (step=0124780/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:23:13] (step=0124800/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:26:05] (step=0124820/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:28:58] (step=0124840/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:31:51] (step=0124860/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:34:46] (step=0124880/epoch=0007) Train Loss: 0.0087, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-21 23:39:36] (step=0124900/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0027, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-21 23:42:29] (step=0124920/epoch=0007) Train Loss: 0.0070, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:45:22] (step=0124940/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:48:15] (step=0124960/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:51:09] (step=0124980/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:54:02] (step=0125000/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:56:55] (step=0125020/epoch=0007) Train Loss: 0.0071, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-21 23:59:47] (step=0125040/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:02:41] (step=0125060/epoch=0007) Train Loss: 0.0089, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:05:34] (step=0125080/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:08:27] (step=0125100/epoch=0007) Train Loss: 0.0077, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:11:20] (step=0125120/epoch=0007) Train Loss: 0.0096, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:14:13] (step=0125140/epoch=0007) Train Loss: 0.0093, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:17:06] (step=0125160/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:19:59] (step=0125180/epoch=0007) Train Loss: 0.0094, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:22:52] (step=0125200/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:25:45] (step=0125220/epoch=0007) Train Loss: 0.0114, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:28:37] (step=0125240/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:31:31] (step=0125260/epoch=0007) Train Loss: 0.0078, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:34:24] (step=0125280/epoch=0007) Train Loss: 0.0074, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:37:17] (step=0125300/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:40:10] (step=0125320/epoch=0007) Train Loss: 0.0098, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:43:03] (step=0125340/epoch=0007) Train Loss: 0.0097, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:45:56] (step=0125360/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:48:48] (step=0125380/epoch=0007) Train Loss: 0.0072, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:51:42] (step=0125400/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:54:36] (step=0125420/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 00:57:29] (step=0125440/epoch=0007) Train Loss: 0.0092, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:00:21] (step=0125460/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:03:14] (step=0125480/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:06:07] (step=0125500/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:09:01] (step=0125520/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0095, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 01:11:55] (step=0125540/epoch=0007) Train Loss: 0.0100, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:14:47] (step=0125560/epoch=0007) Train Loss: 0.0086, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:17:39] (step=0125580/epoch=0007) Train Loss: 0.0064, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:20:32] (step=0125600/epoch=0007) Train Loss: 0.0056, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:23:24] (step=0125620/epoch=0007) Train Loss: 0.0073, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:26:23] (step=0125640/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 01:29:18] (step=0125660/epoch=0007) Train Loss: 0.0084, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 01:32:12] (step=0125680/epoch=0007) Train Loss: 0.0069, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:35:08] (step=0125700/epoch=0007) Train Loss: 0.0088, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 01:38:04] (step=0125720/epoch=0007) Train Loss: 0.0070, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 01:40:58] (step=0125740/epoch=0007) Train Loss: 0.0085, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:43:50] (step=0125760/epoch=0007) Train Loss: 0.0103, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:46:44] (step=0125780/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 01:49:38] (step=0125800/epoch=0007) Train Loss: 0.0079, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:52:32] (step=0125820/epoch=0007) Train Loss: 0.0091, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:55:25] (step=0125840/epoch=0007) Train Loss: 0.0063, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 01:58:19] (step=0125860/epoch=0007) Train Loss: 0.0081, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:01:13] (step=0125880/epoch=0007) Train Loss: 0.0080, Gradient Norm: 0.0107, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 02:04:07] (step=0125900/epoch=0007) Train Loss: 0.0095, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 02:07:00] (step=0125920/epoch=0007) Train Loss: 0.0083, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:09:53] (step=0125940/epoch=0007) Train Loss: 0.0082, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:13:52] (step=0125960/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0139, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 02:16:44] (step=0125980/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:19:36] (step=0126000/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0158, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:23:34] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0126000.pt
[2024-10-22 02:26:26] (step=0126020/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0036, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-22 02:29:25] (step=0126040/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 02:32:21] (step=0126060/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 02:35:15] (step=0126080/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0115, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:38:08] (step=0126100/epoch=0008) Train Loss: 0.0067, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:41:02] (step=0126120/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:44:00] (step=0126140/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 02:47:38] (step=0126160/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0064, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-22 02:50:32] (step=0126180/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 02:53:26] (step=0126200/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:56:19] (step=0126220/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 02:59:13] (step=0126240/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 03:02:06] (step=0126260/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:04:59] (step=0126280/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:07:53] (step=0126300/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:10:46] (step=0126320/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:13:39] (step=0126340/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:17:37] (step=0126360/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0078, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 03:21:38] (step=0126380/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0027, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 03:25:48] (step=0126400/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0042, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 03:28:42] (step=0126420/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 03:31:36] (step=0126440/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:34:28] (step=0126460/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:37:23] (step=0126480/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 03:40:18] (step=0126500/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 03:43:12] (step=0126520/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 03:46:05] (step=0126540/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:48:59] (step=0126560/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:51:52] (step=0126580/epoch=0008) Train Loss: 0.0104, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:54:46] (step=0126600/epoch=0008) Train Loss: 0.0104, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 03:58:56] (step=0126620/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0031, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 04:01:49] (step=0126640/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 04:05:37] (step=0126660/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0028, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-22 04:08:30] (step=0126680/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 04:14:10] (step=0126700/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0028, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-22 04:18:07] (step=0126720/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0074, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 04:21:03] (step=0126740/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:24:15] (step=0126760/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0073, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 04:27:09] (step=0126780/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 04:30:52] (step=0126800/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0075, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-22 04:34:02] (step=0126820/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:37:12] (step=0126840/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:40:33] (step=0126860/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0043, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 04:43:40] (step=0126880/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:46:38] (step=0126900/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:49:35] (step=0126920/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:53:03] (step=0126940/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0037, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 04:56:04] (step=0126960/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 04:59:03] (step=0126980/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:03:39] (step=0127000/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0084, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-22 05:06:44] (step=0127020/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:09:52] (step=0127040/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:12:52] (step=0127060/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:19:02] (step=0127080/epoch=0008) Train Loss: 0.0101, Gradient Norm: 0.0083, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-22 05:21:57] (step=0127100/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:24:51] (step=0127120/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 05:28:16] (step=0127140/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0025, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 05:31:09] (step=0127160/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 05:34:03] (step=0127180/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 05:36:57] (step=0127200/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 05:39:52] (step=0127220/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:42:58] (step=0127240/epoch=0008) Train Loss: 0.0107, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:45:52] (step=0127260/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:48:46] (step=0127280/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:51:56] (step=0127300/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0017, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 05:54:51] (step=0127320/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 05:57:57] (step=0127340/epoch=0008) Train Loss: 0.0102, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:00:50] (step=0127360/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:03:45] (step=0127380/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:06:38] (step=0127400/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0137, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 06:09:32] (step=0127420/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:12:27] (step=0127440/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0101, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:15:21] (step=0127460/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:18:16] (step=0127480/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:21:10] (step=0127500/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:24:03] (step=0127520/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 06:26:57] (step=0127540/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 06:29:51] (step=0127560/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:32:45] (step=0127580/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:35:40] (step=0127600/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:38:34] (step=0127620/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:41:29] (step=0127640/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:44:22] (step=0127660/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 06:47:21] (step=0127680/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:50:15] (step=0127700/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 06:53:10] (step=0127720/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:56:04] (step=0127740/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 06:58:58] (step=0127760/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:01:52] (step=0127780/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:04:46] (step=0127800/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:07:41] (step=0127820/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:10:35] (step=0127840/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:13:28] (step=0127860/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:16:21] (step=0127880/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:19:23] (step=0127900/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:22:17] (step=0127920/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:25:10] (step=0127940/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:28:03] (step=0127960/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:30:57] (step=0127980/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:33:51] (step=0128000/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:37:45] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0128000.pt
[2024-10-22 07:40:38] (step=0128020/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0021, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-22 07:43:31] (step=0128040/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:46:25] (step=0128060/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 07:49:18] (step=0128080/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:52:11] (step=0128100/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:55:05] (step=0128120/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 07:57:57] (step=0128140/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:00:51] (step=0128160/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:04:05] (step=0128180/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0034, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 08:06:59] (step=0128200/epoch=0008) Train Loss: 0.0105, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:09:53] (step=0128220/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:15:13] (step=0128240/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0055, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-22 08:19:22] (step=0128260/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0020, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 08:22:33] (step=0128280/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0074, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 08:25:28] (step=0128300/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:28:22] (step=0128320/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:31:16] (step=0128340/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:34:10] (step=0128360/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:37:03] (step=0128380/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:39:57] (step=0128400/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:42:51] (step=0128420/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:45:46] (step=0128440/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:48:40] (step=0128460/epoch=0008) Train Loss: 0.0101, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:51:34] (step=0128480/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 08:54:28] (step=0128500/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 08:57:22] (step=0128520/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:00:15] (step=0128540/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:03:08] (step=0128560/epoch=0008) Train Loss: 0.0116, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:06:01] (step=0128580/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:08:54] (step=0128600/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:11:48] (step=0128620/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 09:14:41] (step=0128640/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:17:35] (step=0128660/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:20:29] (step=0128680/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:23:22] (step=0128700/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:26:16] (step=0128720/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:29:10] (step=0128740/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 09:32:04] (step=0128760/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 09:34:58] (step=0128780/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:37:52] (step=0128800/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:40:46] (step=0128820/epoch=0008) Train Loss: 0.0101, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 09:43:40] (step=0128840/epoch=0008) Train Loss: 0.0109, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:46:34] (step=0128860/epoch=0008) Train Loss: 0.0066, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 09:49:29] (step=0128880/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 09:52:22] (step=0128900/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:55:15] (step=0128920/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 09:58:10] (step=0128940/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:01:04] (step=0128960/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:03:58] (step=0128980/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:06:52] (step=0129000/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0095, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:09:46] (step=0129020/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:12:39] (step=0129040/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:15:34] (step=0129060/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:18:28] (step=0129080/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:21:22] (step=0129100/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:24:26] (step=0129120/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:27:19] (step=0129140/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:30:12] (step=0129160/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0095, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:33:06] (step=0129180/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:36:00] (step=0129200/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:38:54] (step=0129220/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:41:48] (step=0129240/epoch=0008) Train Loss: 0.0104, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:44:41] (step=0129260/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:47:35] (step=0129280/epoch=0008) Train Loss: 0.0069, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:50:28] (step=0129300/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:53:37] (step=0129320/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 10:56:30] (step=0129340/epoch=0008) Train Loss: 0.0063, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 10:59:55] (step=0129360/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0074, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 11:03:09] (step=0129380/epoch=0008) Train Loss: 0.0116, Gradient Norm: 0.0034, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 11:06:04] (step=0129400/epoch=0008) Train Loss: 0.0110, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:08:58] (step=0129420/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:12:29] (step=0129440/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0086, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-22 11:15:24] (step=0129460/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:18:18] (step=0129480/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:21:15] (step=0129500/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:24:10] (step=0129520/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:27:05] (step=0129540/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:29:59] (step=0129560/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:32:52] (step=0129580/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:35:46] (step=0129600/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:38:40] (step=0129620/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:41:33] (step=0129640/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:44:26] (step=0129660/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:47:20] (step=0129680/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:50:14] (step=0129700/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:53:14] (step=0129720/epoch=0008) Train Loss: 0.0101, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 11:56:07] (step=0129740/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 11:59:01] (step=0129760/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:01:55] (step=0129780/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:04:50] (step=0129800/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:07:44] (step=0129820/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:10:38] (step=0129840/epoch=0008) Train Loss: 0.0105, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:13:32] (step=0129860/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:16:26] (step=0129880/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:19:20] (step=0129900/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:22:13] (step=0129920/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:25:08] (step=0129940/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:28:01] (step=0129960/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:30:55] (step=0129980/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:33:49] (step=0130000/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 12:37:38] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0130000.pt
[2024-10-22 12:40:31] (step=0130020/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0037, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-22 12:43:25] (step=0130040/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:46:20] (step=0130060/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:49:14] (step=0130080/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:52:08] (step=0130100/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:55:05] (step=0130120/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 12:57:59] (step=0130140/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:01:09] (step=0130160/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0067, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 13:04:02] (step=0130180/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 13:09:26] (step=0130200/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0071, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-22 13:14:37] (step=0130220/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0035, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-22 13:17:33] (step=0130240/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:20:28] (step=0130260/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:23:24] (step=0130280/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:26:18] (step=0130300/epoch=0008) Train Loss: 0.0102, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 13:29:13] (step=0130320/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:32:06] (step=0130340/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 13:36:03] (step=0130360/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0065, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 13:38:56] (step=0130380/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0015, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 13:42:17] (step=0130400/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0061, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 13:45:12] (step=0130420/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:48:05] (step=0130440/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 13:51:00] (step=0130460/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:53:54] (step=0130480/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:56:49] (step=0130500/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 13:59:42] (step=0130520/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:02:37] (step=0130540/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:05:32] (step=0130560/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:08:25] (step=0130580/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:11:20] (step=0130600/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:14:12] (step=0130620/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:17:06] (step=0130640/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:19:59] (step=0130660/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:22:54] (step=0130680/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:25:48] (step=0130700/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:28:42] (step=0130720/epoch=0008) Train Loss: 0.0066, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:31:36] (step=0130740/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:34:30] (step=0130760/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:37:25] (step=0130780/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:40:19] (step=0130800/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 14:43:13] (step=0130820/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:46:14] (step=0130840/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:49:09] (step=0130860/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:52:03] (step=0130880/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:54:58] (step=0130900/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 14:57:51] (step=0130920/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:00:44] (step=0130940/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:03:38] (step=0130960/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:06:31] (step=0130980/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:09:26] (step=0131000/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 15:12:19] (step=0131020/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:15:13] (step=0131040/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:18:07] (step=0131060/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 15:21:01] (step=0131080/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:23:54] (step=0131100/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:26:48] (step=0131120/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0097, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 15:29:42] (step=0131140/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:32:35] (step=0131160/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:35:28] (step=0131180/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:38:22] (step=0131200/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0099, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:41:15] (step=0131220/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:44:09] (step=0131240/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 15:47:03] (step=0131260/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:49:57] (step=0131280/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0104, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 15:52:50] (step=0131300/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:55:44] (step=0131320/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 15:58:38] (step=0131340/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:01:33] (step=0131360/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:04:26] (step=0131380/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:07:21] (step=0131400/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:10:15] (step=0131420/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:13:08] (step=0131440/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:16:02] (step=0131460/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:18:56] (step=0131480/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:21:51] (step=0131500/epoch=0008) Train Loss: 0.0068, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:24:44] (step=0131520/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:27:38] (step=0131540/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:30:31] (step=0131560/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:33:25] (step=0131580/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:36:18] (step=0131600/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:39:11] (step=0131620/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:42:05] (step=0131640/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 16:47:56] (step=0131660/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-22 16:50:50] (step=0131680/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:53:43] (step=0131700/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:56:37] (step=0131720/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 16:59:31] (step=0131740/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:02:26] (step=0131760/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0116, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:05:20] (step=0131780/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:08:14] (step=0131800/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:12:41] (step=0131820/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0036, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 17:15:35] (step=0131840/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:18:29] (step=0131860/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:21:22] (step=0131880/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:24:17] (step=0131900/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:27:10] (step=0131920/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:30:05] (step=0131940/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:32:59] (step=0131960/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:35:52] (step=0131980/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:38:46] (step=0132000/epoch=0008) Train Loss: 0.0104, Gradient Norm: 0.0104, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:42:58] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0132000.pt
[2024-10-22 17:45:50] (step=0132020/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0038, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-22 17:48:44] (step=0132040/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:51:37] (step=0132060/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 17:54:32] (step=0132080/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 17:57:25] (step=0132100/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:00:25] (step=0132120/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:03:19] (step=0132140/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:06:13] (step=0132160/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:09:08] (step=0132180/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:12:01] (step=0132200/epoch=0008) Train Loss: 0.0106, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:14:55] (step=0132220/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:17:49] (step=0132240/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:20:42] (step=0132260/epoch=0008) Train Loss: 0.0107, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:23:35] (step=0132280/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:26:58] (step=0132300/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0022, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 18:29:52] (step=0132320/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:32:47] (step=0132340/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:35:41] (step=0132360/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:38:35] (step=0132380/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:41:29] (step=0132400/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:44:22] (step=0132420/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:47:17] (step=0132440/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:50:10] (step=0132460/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 18:53:05] (step=0132480/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:56:00] (step=0132500/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 18:59:43] (step=0132520/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0062, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-22 19:02:37] (step=0132540/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:05:30] (step=0132560/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:08:24] (step=0132580/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:11:18] (step=0132600/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:14:29] (step=0132620/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0035, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 19:17:23] (step=0132640/epoch=0008) Train Loss: 0.0104, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:20:18] (step=0132660/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:23:12] (step=0132680/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:26:06] (step=0132700/epoch=0008) Train Loss: 0.0106, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:29:00] (step=0132720/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:31:53] (step=0132740/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:34:47] (step=0132760/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:38:10] (step=0132780/epoch=0008) Train Loss: 0.0105, Gradient Norm: 0.0033, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 19:41:04] (step=0132800/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:43:58] (step=0132820/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:46:52] (step=0132840/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:49:46] (step=0132860/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:52:40] (step=0132880/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 19:55:35] (step=0132900/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 19:58:28] (step=0132920/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 20:01:23] (step=0132940/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:04:16] (step=0132960/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 20:07:10] (step=0132980/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:10:04] (step=0133000/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 20:12:58] (step=0133020/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:17:09] (step=0133040/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0070, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 20:20:57] (step=0133060/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0036, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-22 20:25:08] (step=0133080/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0094, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 20:28:03] (step=0133100/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:30:58] (step=0133120/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:33:51] (step=0133140/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 20:36:45] (step=0133160/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:39:39] (step=0133180/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 20:42:33] (step=0133200/epoch=0008) Train Loss: 0.0108, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:45:27] (step=0133220/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:48:22] (step=0133240/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:51:17] (step=0133260/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 20:54:11] (step=0133280/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 20:57:38] (step=0133300/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0045, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-22 21:00:31] (step=0133320/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:03:35] (step=0133340/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:06:29] (step=0133360/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:09:23] (step=0133380/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:12:18] (step=0133400/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:16:23] (step=0133420/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0028, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-22 21:19:18] (step=0133440/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:22:12] (step=0133460/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:25:06] (step=0133480/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:28:00] (step=0133500/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:30:54] (step=0133520/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:33:49] (step=0133540/epoch=0008) Train Loss: 0.0106, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:36:43] (step=0133560/epoch=0008) Train Loss: 0.0107, Gradient Norm: 0.0114, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:39:38] (step=0133580/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 21:42:31] (step=0133600/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0112, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:45:25] (step=0133620/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:48:18] (step=0133640/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:51:12] (step=0133660/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:54:06] (step=0133680/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:56:59] (step=0133700/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 21:59:53] (step=0133720/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:02:48] (step=0133740/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:05:45] (step=0133760/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:08:39] (step=0133780/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:11:33] (step=0133800/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:14:26] (step=0133820/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:17:20] (step=0133840/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:20:13] (step=0133860/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:23:07] (step=0133880/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:26:00] (step=0133900/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:28:55] (step=0133920/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:31:48] (step=0133940/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:34:42] (step=0133960/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:37:36] (step=0133980/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:40:30] (step=0134000/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:44:33] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0134000.pt
[2024-10-22 22:47:27] (step=0134020/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0022, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-22 22:50:20] (step=0134040/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:53:15] (step=0134060/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 22:56:08] (step=0134080/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 22:59:02] (step=0134100/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:01:56] (step=0134120/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:04:49] (step=0134140/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:07:42] (step=0134160/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:10:35] (step=0134180/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:13:29] (step=0134200/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:16:23] (step=0134220/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:19:17] (step=0134240/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:22:10] (step=0134260/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:25:04] (step=0134280/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:27:58] (step=0134300/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:30:52] (step=0134320/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:33:45] (step=0134340/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:36:39] (step=0134360/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:39:34] (step=0134380/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:42:27] (step=0134400/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:45:21] (step=0134420/epoch=0008) Train Loss: 0.0108, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:48:15] (step=0134440/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-22 23:51:17] (step=0134460/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:54:12] (step=0134480/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-22 23:57:06] (step=0134500/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:00:00] (step=0134520/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:02:53] (step=0134540/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:05:47] (step=0134560/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 00:08:40] (step=0134580/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:11:35] (step=0134600/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 00:14:28] (step=0134620/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:17:21] (step=0134640/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:20:15] (step=0134660/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:23:09] (step=0134680/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:29:02] (step=0134700/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0032, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-23 00:32:10] (step=0134720/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 00:35:41] (step=0134740/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0031, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-23 00:38:33] (step=0134760/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:42:00] (step=0134780/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0026, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 00:44:54] (step=0134800/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:47:47] (step=0134820/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:50:41] (step=0134840/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:53:34] (step=0134860/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:56:28] (step=0134880/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 00:59:21] (step=0134900/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:02:26] (step=0134920/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:05:20] (step=0134940/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:08:14] (step=0134960/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:11:08] (step=0134980/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:14:01] (step=0135000/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:16:59] (step=0135020/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:19:52] (step=0135040/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:22:46] (step=0135060/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:25:38] (step=0135080/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:28:31] (step=0135100/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:31:24] (step=0135120/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:34:19] (step=0135140/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:37:12] (step=0135160/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:40:05] (step=0135180/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:42:59] (step=0135200/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:45:51] (step=0135220/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:48:46] (step=0135240/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:51:38] (step=0135260/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 01:54:43] (step=0135280/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 01:58:17] (step=0135300/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0038, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-23 02:01:12] (step=0135320/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:04:06] (step=0135340/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:07:00] (step=0135360/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:09:55] (step=0135380/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:12:48] (step=0135400/epoch=0008) Train Loss: 0.0067, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:15:42] (step=0135420/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:18:35] (step=0135440/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:21:29] (step=0135460/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:24:22] (step=0135480/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:27:15] (step=0135500/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:30:09] (step=0135520/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:33:03] (step=0135540/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:35:58] (step=0135560/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:38:51] (step=0135580/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:41:44] (step=0135600/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:44:38] (step=0135620/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:47:31] (step=0135640/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 02:50:26] (step=0135660/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:53:22] (step=0135680/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:56:25] (step=0135700/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 02:59:21] (step=0135720/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 03:02:15] (step=0135740/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:05:08] (step=0135760/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:08:01] (step=0135780/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:10:54] (step=0135800/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:13:48] (step=0135820/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:16:42] (step=0135840/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:19:35] (step=0135860/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 03:22:57] (step=0135880/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0080, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 03:25:55] (step=0135900/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 03:29:04] (step=0135920/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 03:31:58] (step=0135940/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 03:36:43] (step=0135960/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0074, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-23 03:40:49] (step=0135980/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0045, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-23 03:47:11] (step=0136000/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0088, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-23 03:51:02] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0136000.pt
[2024-10-23 03:53:56] (step=0136020/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0048, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-23 03:56:50] (step=0136040/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 03:59:44] (step=0136060/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:02:37] (step=0136080/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:05:41] (step=0136100/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:08:34] (step=0136120/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:11:29] (step=0136140/epoch=0008) Train Loss: 0.0101, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:14:22] (step=0136160/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:17:17] (step=0136180/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:20:42] (step=0136200/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0067, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 04:23:36] (step=0136220/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:26:29] (step=0136240/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:29:23] (step=0136260/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:32:17] (step=0136280/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:35:10] (step=0136300/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:38:05] (step=0136320/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:40:59] (step=0136340/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:43:54] (step=0136360/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 04:46:48] (step=0136380/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:50:04] (step=0136400/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0069, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 04:52:58] (step=0136420/epoch=0008) Train Loss: 0.0063, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:55:51] (step=0136440/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 04:58:57] (step=0136460/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:01:50] (step=0136480/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:04:44] (step=0136500/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:07:37] (step=0136520/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:17:27] (step=0136540/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0026, Train Steps/Sec: 0.03, lr: 0.000100
[2024-10-23 05:20:20] (step=0136560/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:23:15] (step=0136580/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:26:09] (step=0136600/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:29:02] (step=0136620/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:31:56] (step=0136640/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:34:50] (step=0136660/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:37:45] (step=0136680/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:40:39] (step=0136700/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:43:32] (step=0136720/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:46:26] (step=0136740/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:49:20] (step=0136760/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:52:14] (step=0136780/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 05:55:08] (step=0136800/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 05:58:02] (step=0136820/epoch=0008) Train Loss: 0.0106, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:00:56] (step=0136840/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:03:50] (step=0136860/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:06:44] (step=0136880/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:09:39] (step=0136900/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:12:32] (step=0136920/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:15:26] (step=0136940/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:18:20] (step=0136960/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:21:15] (step=0136980/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:24:08] (step=0137000/epoch=0008) Train Loss: 0.0067, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:27:02] (step=0137020/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:29:55] (step=0137040/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:32:48] (step=0137060/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:35:42] (step=0137080/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:38:35] (step=0137100/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:41:29] (step=0137120/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:44:24] (step=0137140/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:47:18] (step=0137160/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:50:12] (step=0137180/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:53:07] (step=0137200/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 06:56:00] (step=0137220/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 06:58:53] (step=0137240/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:01:51] (step=0137260/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:04:44] (step=0137280/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:08:42] (step=0137300/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0028, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-23 07:11:36] (step=0137320/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:14:29] (step=0137340/epoch=0008) Train Loss: 0.0112, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:17:24] (step=0137360/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0097, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:20:17] (step=0137380/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:23:12] (step=0137400/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:26:05] (step=0137420/epoch=0008) Train Loss: 0.0102, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:29:00] (step=0137440/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:31:53] (step=0137460/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:34:48] (step=0137480/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:37:42] (step=0137500/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:40:36] (step=0137520/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:43:30] (step=0137540/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:46:23] (step=0137560/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:49:17] (step=0137580/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 07:52:10] (step=0137600/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 07:56:33] (step=0137620/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0024, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-23 07:59:26] (step=0137640/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:02:20] (step=0137660/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:05:14] (step=0137680/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:08:09] (step=0137700/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:11:02] (step=0137720/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:14:06] (step=0137740/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:17:00] (step=0137760/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:19:53] (step=0137780/epoch=0008) Train Loss: 0.0065, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:22:47] (step=0137800/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:25:40] (step=0137820/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:28:35] (step=0137840/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:31:28] (step=0137860/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:34:21] (step=0137880/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:37:15] (step=0137900/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:40:08] (step=0137920/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:43:02] (step=0137940/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 08:46:01] (step=0137960/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:48:56] (step=0137980/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:51:55] (step=0138000/epoch=0008) Train Loss: 0.0068, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 08:55:48] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0138000.pt
[2024-10-23 09:00:32] (step=0138020/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0031, Train Steps/Sec: 0.04, lr: 0.000100
[2024-10-23 09:04:23] (step=0138040/epoch=0008) Train Loss: 0.0068, Gradient Norm: 0.0059, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-23 09:07:18] (step=0138060/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:10:13] (step=0138080/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:13:06] (step=0138100/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 09:16:00] (step=0138120/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:18:53] (step=0138140/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 09:21:48] (step=0138160/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:24:42] (step=0138180/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:27:36] (step=0138200/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 09:30:30] (step=0138220/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 09:33:24] (step=0138240/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 09:36:18] (step=0138260/epoch=0008) Train Loss: 0.0111, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:39:11] (step=0138280/epoch=0008) Train Loss: 0.0102, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 09:42:05] (step=0138300/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:45:16] (step=0138320/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0068, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 09:48:11] (step=0138340/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:51:05] (step=0138360/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:53:59] (step=0138380/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:56:54] (step=0138400/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 09:59:49] (step=0138420/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:02:42] (step=0138440/epoch=0008) Train Loss: 0.0110, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:05:36] (step=0138460/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:08:36] (step=0138480/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:11:30] (step=0138500/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:14:25] (step=0138520/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:17:19] (step=0138540/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:20:14] (step=0138560/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:23:08] (step=0138580/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:26:01] (step=0138600/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:28:54] (step=0138620/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:31:49] (step=0138640/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:34:43] (step=0138660/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:37:36] (step=0138680/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:40:31] (step=0138700/epoch=0008) Train Loss: 0.0071, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:43:24] (step=0138720/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:46:18] (step=0138740/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:49:13] (step=0138760/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:52:06] (step=0138780/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 10:55:01] (step=0138800/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 10:57:54] (step=0138820/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:00:48] (step=0138840/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:03:41] (step=0138860/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:06:47] (step=0138880/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:09:40] (step=0138900/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:12:34] (step=0138920/epoch=0008) Train Loss: 0.0068, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:15:28] (step=0138940/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:18:22] (step=0138960/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:21:17] (step=0138980/epoch=0008) Train Loss: 0.0066, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:24:11] (step=0139000/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:27:04] (step=0139020/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:29:58] (step=0139040/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:32:52] (step=0139060/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:35:47] (step=0139080/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:38:40] (step=0139100/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:41:35] (step=0139120/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:44:29] (step=0139140/epoch=0008) Train Loss: 0.0098, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:47:24] (step=0139160/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:50:17] (step=0139180/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:53:12] (step=0139200/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 11:56:05] (step=0139220/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 11:59:00] (step=0139240/epoch=0008) Train Loss: 0.0095, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:01:54] (step=0139260/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 12:04:48] (step=0139280/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 12:07:42] (step=0139300/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:10:36] (step=0139320/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:13:33] (step=0139340/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:16:37] (step=0139360/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0113, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:19:32] (step=0139380/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:22:24] (step=0139400/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 12:25:21] (step=0139420/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:28:15] (step=0139440/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:31:13] (step=0139460/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:34:06] (step=0139480/epoch=0008) Train Loss: 0.0066, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 12:37:01] (step=0139500/epoch=0008) Train Loss: 0.0106, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:39:55] (step=0139520/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 12:43:03] (step=0139540/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:45:57] (step=0139560/epoch=0008) Train Loss: 0.0068, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:48:52] (step=0139580/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:51:46] (step=0139600/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:54:40] (step=0139620/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 12:57:34] (step=0139640/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:00:27] (step=0139660/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:03:21] (step=0139680/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:06:17] (step=0139700/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:09:10] (step=0139720/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:12:05] (step=0139740/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:15:02] (step=0139760/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:17:56] (step=0139780/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:20:50] (step=0139800/epoch=0008) Train Loss: 0.0093, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:23:44] (step=0139820/epoch=0008) Train Loss: 0.0070, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:26:38] (step=0139840/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:29:32] (step=0139860/epoch=0008) Train Loss: 0.0107, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:32:27] (step=0139880/epoch=0008) Train Loss: 0.0099, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:35:21] (step=0139900/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:38:16] (step=0139920/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:41:12] (step=0139940/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:44:09] (step=0139960/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:47:03] (step=0139980/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 13:49:56] (step=0140000/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0115, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 13:54:08] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0140000.pt
[2024-10-23 13:57:01] (step=0140020/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0036, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-23 13:59:54] (step=0140040/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 14:02:48] (step=0140060/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 14:05:42] (step=0140080/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:08:36] (step=0140100/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:11:35] (step=0140120/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:14:40] (step=0140140/epoch=0008) Train Loss: 0.0091, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:17:32] (step=0140160/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 14:20:53] (step=0140180/epoch=0008) Train Loss: 0.0079, Gradient Norm: 0.0026, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 14:23:46] (step=0140200/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 14:28:07] (step=0140220/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0029, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-23 14:31:44] (step=0140240/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0075, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-23 14:35:30] (step=0140260/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0028, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-23 14:38:30] (step=0140280/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:41:24] (step=0140300/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:44:18] (step=0140320/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:47:12] (step=0140340/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:50:07] (step=0140360/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:53:01] (step=0140380/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:55:55] (step=0140400/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 14:58:50] (step=0140420/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:01:43] (step=0140440/epoch=0008) Train Loss: 0.0066, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:04:51] (step=0140460/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:07:44] (step=0140480/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:10:38] (step=0140500/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:13:32] (step=0140520/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:16:26] (step=0140540/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:19:20] (step=0140560/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:22:13] (step=0140580/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:25:07] (step=0140600/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:27:59] (step=0140620/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:30:54] (step=0140640/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:33:56] (step=0140660/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:36:50] (step=0140680/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:39:45] (step=0140700/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:42:38] (step=0140720/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:45:32] (step=0140740/epoch=0008) Train Loss: 0.0104, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:48:27] (step=0140760/epoch=0008) Train Loss: 0.0097, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:51:21] (step=0140780/epoch=0008) Train Loss: 0.0089, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 15:54:14] (step=0140800/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 15:57:08] (step=0140820/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:00:02] (step=0140840/epoch=0008) Train Loss: 0.0069, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 16:02:56] (step=0140860/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:06:22] (step=0140880/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0069, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 16:09:17] (step=0140900/epoch=0008) Train Loss: 0.0103, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:12:11] (step=0140920/epoch=0008) Train Loss: 0.0094, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:15:06] (step=0140940/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:17:59] (step=0140960/epoch=0008) Train Loss: 0.0074, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 16:20:52] (step=0140980/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 16:23:46] (step=0141000/epoch=0008) Train Loss: 0.0090, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:27:12] (step=0141020/epoch=0008) Train Loss: 0.0086, Gradient Norm: 0.0033, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 16:30:06] (step=0141040/epoch=0008) Train Loss: 0.0096, Gradient Norm: 0.0099, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 16:33:00] (step=0141060/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:36:01] (step=0141080/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:38:55] (step=0141100/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:41:49] (step=0141120/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:44:43] (step=0141140/epoch=0008) Train Loss: 0.0088, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:47:37] (step=0141160/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:50:33] (step=0141180/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:53:26] (step=0141200/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 16:56:20] (step=0141220/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 16:59:15] (step=0141240/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:02:09] (step=0141260/epoch=0008) Train Loss: 0.0092, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:05:03] (step=0141280/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:07:57] (step=0141300/epoch=0008) Train Loss: 0.0084, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:10:51] (step=0141320/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:13:45] (step=0141340/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:16:39] (step=0141360/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:19:32] (step=0141380/epoch=0008) Train Loss: 0.0080, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:22:26] (step=0141400/epoch=0008) Train Loss: 0.0072, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:25:21] (step=0141420/epoch=0008) Train Loss: 0.0077, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:28:48] (step=0141440/epoch=0008) Train Loss: 0.0085, Gradient Norm: 0.0074, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 17:31:41] (step=0141460/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:34:49] (step=0141480/epoch=0008) Train Loss: 0.0069, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:37:43] (step=0141500/epoch=0008) Train Loss: 0.0078, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:40:51] (step=0141520/epoch=0008) Train Loss: 0.0075, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:43:46] (step=0141540/epoch=0008) Train Loss: 0.0081, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:46:38] (step=0141560/epoch=0008) Train Loss: 0.0087, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:49:33] (step=0141580/epoch=0008) Train Loss: 0.0073, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 17:52:27] (step=0141600/epoch=0008) Train Loss: 0.0076, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:55:20] (step=0141620/epoch=0008) Train Loss: 0.0083, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 17:58:14] (step=0141640/epoch=0008) Train Loss: 0.0082, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:01:07] (step=0141660/epoch=0008) Train Loss: 0.0100, Gradient Norm: 0.0025, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:04:02] (step=0141680/epoch=0008) Train Loss: 0.0066, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 18:08:01] (step=0141700/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0034, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-23 18:10:53] (step=0141720/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:13:45] (step=0141740/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:16:37] (step=0141760/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0108, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:19:30] (step=0141780/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:22:22] (step=0141800/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:25:19] (step=0141820/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 18:28:12] (step=0141840/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:31:06] (step=0141860/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 18:33:59] (step=0141880/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0115, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:36:53] (step=0141900/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:39:48] (step=0141920/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 18:42:41] (step=0141940/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:45:35] (step=0141960/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:48:28] (step=0141980/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:51:22] (step=0142000/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 18:55:42] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0142000.pt
[2024-10-23 18:58:35] (step=0142020/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0066, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-23 19:01:29] (step=0142040/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:04:22] (step=0142060/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:07:15] (step=0142080/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:10:25] (step=0142100/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:13:17] (step=0142120/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:16:11] (step=0142140/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:19:05] (step=0142160/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:21:59] (step=0142180/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:24:52] (step=0142200/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:27:46] (step=0142220/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:30:40] (step=0142240/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:33:34] (step=0142260/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:36:28] (step=0142280/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:39:21] (step=0142300/epoch=0009) Train Loss: 0.0102, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:42:15] (step=0142320/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:45:16] (step=0142340/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:48:10] (step=0142360/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:51:03] (step=0142380/epoch=0009) Train Loss: 0.0115, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 19:53:58] (step=0142400/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:56:52] (step=0142420/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 19:59:46] (step=0142440/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:02:40] (step=0142460/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:05:33] (step=0142480/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:08:27] (step=0142500/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:11:20] (step=0142520/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:14:14] (step=0142540/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 20:17:08] (step=0142560/epoch=0009) Train Loss: 0.0117, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 20:20:02] (step=0142580/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:22:55] (step=0142600/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:25:48] (step=0142620/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:28:42] (step=0142640/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 20:31:36] (step=0142660/epoch=0009) Train Loss: 0.0103, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:34:29] (step=0142680/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:37:23] (step=0142700/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:40:17] (step=0142720/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 20:43:11] (step=0142740/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:46:05] (step=0142760/epoch=0009) Train Loss: 0.0103, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 20:48:58] (step=0142780/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:51:52] (step=0142800/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:54:45] (step=0142820/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 20:57:39] (step=0142840/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0114, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:00:33] (step=0142860/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:03:26] (step=0142880/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0111, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:06:21] (step=0142900/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:09:14] (step=0142920/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:12:08] (step=0142940/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:15:02] (step=0142960/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0095, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:18:10] (step=0142980/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:21:26] (step=0143000/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0178, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 21:24:20] (step=0143020/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0101, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:27:14] (step=0143040/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0156, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:30:07] (step=0143060/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:33:02] (step=0143080/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0145, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:35:55] (step=0143100/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:38:49] (step=0143120/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0114, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:42:17] (step=0143140/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0059, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 21:45:11] (step=0143160/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:48:04] (step=0143180/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:50:58] (step=0143200/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0136, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:53:52] (step=0143220/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 21:56:46] (step=0143240/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 21:59:40] (step=0143260/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:02:33] (step=0143280/epoch=0009) Train Loss: 0.0105, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:06:43] (step=0143300/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0051, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-23 22:09:36] (step=0143320/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:12:45] (step=0143340/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 22:15:39] (step=0143360/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 22:18:32] (step=0143380/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:21:25] (step=0143400/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:24:19] (step=0143420/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:27:13] (step=0143440/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 22:30:21] (step=0143460/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 22:33:15] (step=0143480/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:36:45] (step=0143500/epoch=0009) Train Loss: 0.0064, Gradient Norm: 0.0051, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-23 22:39:39] (step=0143520/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:42:32] (step=0143540/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:45:26] (step=0143560/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 22:48:19] (step=0143580/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 22:51:14] (step=0143600/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 22:54:08] (step=0143620/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:00:04] (step=0143640/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0063, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-23 23:02:57] (step=0143660/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:05:50] (step=0143680/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:08:44] (step=0143700/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:11:37] (step=0143720/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:14:30] (step=0143740/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:17:24] (step=0143760/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:20:18] (step=0143780/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:23:12] (step=0143800/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:26:05] (step=0143820/epoch=0009) Train Loss: 0.0062, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:28:58] (step=0143840/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:31:53] (step=0143860/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:34:47] (step=0143880/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:37:41] (step=0143900/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:40:35] (step=0143920/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:43:30] (step=0143940/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:46:22] (step=0143960/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0095, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:49:23] (step=0143980/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-23 23:52:16] (step=0144000/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-23 23:56:11] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0144000.pt
[2024-10-23 23:59:04] (step=0144020/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0042, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-24 00:01:58] (step=0144040/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:04:51] (step=0144060/epoch=0009) Train Loss: 0.0067, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:07:45] (step=0144080/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:10:38] (step=0144100/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:13:32] (step=0144120/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:16:26] (step=0144140/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 00:19:19] (step=0144160/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:22:13] (step=0144180/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 00:25:07] (step=0144200/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:28:00] (step=0144220/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:30:53] (step=0144240/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:33:46] (step=0144260/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:36:47] (step=0144280/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 00:39:40] (step=0144300/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:42:37] (step=0144320/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 00:45:31] (step=0144340/epoch=0009) Train Loss: 0.0106, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:48:25] (step=0144360/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 00:51:19] (step=0144380/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 00:54:13] (step=0144400/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 00:57:07] (step=0144420/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:00:01] (step=0144440/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:02:54] (step=0144460/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:05:48] (step=0144480/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:08:42] (step=0144500/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:11:36] (step=0144520/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:14:29] (step=0144540/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:17:22] (step=0144560/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:20:15] (step=0144580/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:23:09] (step=0144600/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:26:03] (step=0144620/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:28:56] (step=0144640/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:31:49] (step=0144660/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:34:42] (step=0144680/epoch=0009) Train Loss: 0.0102, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:37:38] (step=0144700/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:40:50] (step=0144720/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0076, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 01:43:43] (step=0144740/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:46:37] (step=0144760/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0101, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:49:32] (step=0144780/epoch=0009) Train Loss: 0.0066, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:52:25] (step=0144800/epoch=0009) Train Loss: 0.0107, Gradient Norm: 0.0113, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 01:55:28] (step=0144820/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 01:58:22] (step=0144840/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:01:17] (step=0144860/epoch=0009) Train Loss: 0.0063, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:04:11] (step=0144880/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:07:05] (step=0144900/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:09:58] (step=0144920/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:13:36] (step=0144940/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0069, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-24 02:16:30] (step=0144960/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:19:25] (step=0144980/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:22:19] (step=0145000/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0101, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:25:14] (step=0145020/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:28:08] (step=0145040/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0124, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:31:02] (step=0145060/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:33:55] (step=0145080/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:36:49] (step=0145100/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:39:43] (step=0145120/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:44:08] (step=0145140/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0040, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-24 02:47:00] (step=0145160/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:49:54] (step=0145180/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:52:48] (step=0145200/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 02:55:43] (step=0145220/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 02:58:38] (step=0145240/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 03:01:32] (step=0145260/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:04:25] (step=0145280/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:07:19] (step=0145300/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:10:12] (step=0145320/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:13:06] (step=0145340/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:15:59] (step=0145360/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:18:52] (step=0145380/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:21:47] (step=0145400/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 03:24:42] (step=0145420/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 03:28:04] (step=0145440/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0071, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 03:30:58] (step=0145460/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 03:33:50] (step=0145480/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:36:44] (step=0145500/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:39:37] (step=0145520/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:42:31] (step=0145540/epoch=0009) Train Loss: 0.0103, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 03:45:24] (step=0145560/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:48:18] (step=0145580/epoch=0009) Train Loss: 0.0068, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:51:13] (step=0145600/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 03:54:07] (step=0145620/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:57:00] (step=0145640/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 03:59:54] (step=0145660/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:02:47] (step=0145680/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:11:04] (step=0145700/epoch=0009) Train Loss: 0.0059, Gradient Norm: 0.0028, Train Steps/Sec: 0.04, lr: 0.000100
[2024-10-24 04:13:58] (step=0145720/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:16:52] (step=0145740/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:19:46] (step=0145760/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:22:40] (step=0145780/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:25:34] (step=0145800/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:28:28] (step=0145820/epoch=0009) Train Loss: 0.0101, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:31:22] (step=0145840/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:34:16] (step=0145860/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:37:09] (step=0145880/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:40:02] (step=0145900/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:42:55] (step=0145920/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:45:50] (step=0145940/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:48:44] (step=0145960/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 04:51:38] (step=0145980/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:54:32] (step=0146000/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 04:58:17] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0146000.pt
[2024-10-24 05:01:09] (step=0146020/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0041, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-24 05:04:03] (step=0146040/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:06:57] (step=0146060/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:09:51] (step=0146080/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:12:45] (step=0146100/epoch=0009) Train Loss: 0.0065, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:15:39] (step=0146120/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:18:33] (step=0146140/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:21:26] (step=0146160/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:24:19] (step=0146180/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:27:13] (step=0146200/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:30:07] (step=0146220/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:33:00] (step=0146240/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:35:55] (step=0146260/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:38:51] (step=0146280/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:41:46] (step=0146300/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:44:40] (step=0146320/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:47:33] (step=0146340/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:50:27] (step=0146360/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 05:53:21] (step=0146380/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:56:15] (step=0146400/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 05:59:08] (step=0146420/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:02:02] (step=0146440/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:04:56] (step=0146460/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:07:50] (step=0146480/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:10:45] (step=0146500/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:13:38] (step=0146520/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:16:31] (step=0146540/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:19:25] (step=0146560/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:22:20] (step=0146580/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:25:13] (step=0146600/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:28:07] (step=0146620/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:31:01] (step=0146640/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:33:56] (step=0146660/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:36:49] (step=0146680/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:39:42] (step=0146700/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:42:36] (step=0146720/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:45:29] (step=0146740/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:48:23] (step=0146760/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:51:24] (step=0146780/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 06:54:17] (step=0146800/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 06:57:17] (step=0146820/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:00:11] (step=0146840/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:03:05] (step=0146860/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:05:59] (step=0146880/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:08:52] (step=0146900/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:11:47] (step=0146920/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:14:40] (step=0146940/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:17:34] (step=0146960/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:20:29] (step=0146980/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:23:23] (step=0147000/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:26:17] (step=0147020/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:29:11] (step=0147040/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:32:04] (step=0147060/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:34:58] (step=0147080/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:37:51] (step=0147100/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:40:45] (step=0147120/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:43:37] (step=0147140/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:46:32] (step=0147160/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 07:49:24] (step=0147180/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:52:17] (step=0147200/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:55:10] (step=0147220/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 07:58:03] (step=0147240/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0120, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:00:58] (step=0147260/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:03:51] (step=0147280/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:06:45] (step=0147300/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:09:39] (step=0147320/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:12:33] (step=0147340/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:15:27] (step=0147360/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:18:20] (step=0147380/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:21:14] (step=0147400/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0112, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:24:08] (step=0147420/epoch=0009) Train Loss: 0.0064, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:27:01] (step=0147440/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:29:54] (step=0147460/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:32:48] (step=0147480/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:35:41] (step=0147500/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:38:36] (step=0147520/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:41:31] (step=0147540/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:44:25] (step=0147560/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 08:47:19] (step=0147580/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:51:11] (step=0147600/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0067, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-24 08:54:04] (step=0147620/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:56:57] (step=0147640/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 08:59:51] (step=0147660/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:02:46] (step=0147680/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:05:40] (step=0147700/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:08:34] (step=0147720/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:11:28] (step=0147740/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:14:23] (step=0147760/epoch=0009) Train Loss: 0.0102, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:17:17] (step=0147780/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:20:11] (step=0147800/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:23:05] (step=0147820/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:25:59] (step=0147840/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:28:52] (step=0147860/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:31:46] (step=0147880/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:34:40] (step=0147900/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:37:34] (step=0147920/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:40:28] (step=0147940/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:43:21] (step=0147960/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 09:46:16] (step=0147980/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0030, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:49:10] (step=0148000/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 09:53:08] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0148000.pt
[2024-10-24 09:56:01] (step=0148020/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0052, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-24 09:58:54] (step=0148040/epoch=0009) Train Loss: 0.0101, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:01:48] (step=0148060/epoch=0009) Train Loss: 0.0066, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:04:42] (step=0148080/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:07:36] (step=0148100/epoch=0009) Train Loss: 0.0101, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:10:30] (step=0148120/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:13:22] (step=0148140/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:16:17] (step=0148160/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:19:11] (step=0148180/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:22:05] (step=0148200/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:24:58] (step=0148220/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:27:52] (step=0148240/epoch=0009) Train Loss: 0.0105, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:30:45] (step=0148260/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:33:39] (step=0148280/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:36:32] (step=0148300/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:39:25] (step=0148320/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:42:19] (step=0148340/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:45:19] (step=0148360/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 10:48:12] (step=0148380/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:51:05] (step=0148400/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:53:58] (step=0148420/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:56:50] (step=0148440/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 10:59:44] (step=0148460/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:02:38] (step=0148480/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:05:31] (step=0148500/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:08:25] (step=0148520/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:11:19] (step=0148540/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:14:13] (step=0148560/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:17:06] (step=0148580/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:20:00] (step=0148600/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:22:53] (step=0148620/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:25:48] (step=0148640/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:28:42] (step=0148660/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:31:35] (step=0148680/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:34:29] (step=0148700/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:37:22] (step=0148720/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:40:16] (step=0148740/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:43:09] (step=0148760/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:46:03] (step=0148780/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:48:57] (step=0148800/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 11:51:51] (step=0148820/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:54:49] (step=0148840/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 11:57:42] (step=0148860/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:00:35] (step=0148880/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:03:28] (step=0148900/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:06:23] (step=0148920/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:09:17] (step=0148940/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:12:11] (step=0148960/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:15:04] (step=0148980/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:17:58] (step=0149000/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0097, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:21:02] (step=0149020/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:23:55] (step=0149040/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:26:48] (step=0149060/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:29:42] (step=0149080/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:33:54] (step=0149100/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0044, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-24 12:39:17] (step=0149120/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0082, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-24 12:42:12] (step=0149140/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:45:05] (step=0149160/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:48:00] (step=0149180/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:50:54] (step=0149200/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:53:48] (step=0149220/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 12:56:42] (step=0149240/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 12:59:35] (step=0149260/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:02:29] (step=0149280/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:05:22] (step=0149300/epoch=0009) Train Loss: 0.0068, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:08:16] (step=0149320/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:11:09] (step=0149340/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:14:04] (step=0149360/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:16:58] (step=0149380/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:19:52] (step=0149400/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:22:45] (step=0149420/epoch=0009) Train Loss: 0.0064, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:25:38] (step=0149440/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:28:32] (step=0149460/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:31:27] (step=0149480/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0157, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:34:21] (step=0149500/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:37:14] (step=0149520/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0118, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:40:08] (step=0149540/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 13:43:00] (step=0149560/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:45:54] (step=0149580/epoch=0009) Train Loss: 0.0066, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:48:46] (step=0149600/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0113, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:52:12] (step=0149620/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0049, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 13:55:06] (step=0149640/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 13:58:00] (step=0149660/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:01:11] (step=0149680/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0063, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 14:04:05] (step=0149700/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:06:58] (step=0149720/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:09:52] (step=0149740/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:12:46] (step=0149760/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:15:40] (step=0149780/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:18:33] (step=0149800/epoch=0009) Train Loss: 0.0063, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:21:27] (step=0149820/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:24:21] (step=0149840/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:27:15] (step=0149860/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:30:10] (step=0149880/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:33:04] (step=0149900/epoch=0009) Train Loss: 0.0066, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:35:58] (step=0149920/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:38:51] (step=0149940/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:41:45] (step=0149960/epoch=0009) Train Loss: 0.0107, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 14:44:39] (step=0149980/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:47:33] (step=0150000/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 14:51:22] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0150000.pt
[2024-10-24 14:54:14] (step=0150020/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0032, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-24 14:57:08] (step=0150040/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:00:01] (step=0150060/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:02:55] (step=0150080/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:05:48] (step=0150100/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:08:42] (step=0150120/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:11:37] (step=0150140/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:14:44] (step=0150160/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:17:38] (step=0150180/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0035, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:20:30] (step=0150200/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:23:24] (step=0150220/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:26:17] (step=0150240/epoch=0009) Train Loss: 0.0111, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:29:12] (step=0150260/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:32:06] (step=0150280/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0118, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:34:59] (step=0150300/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:38:12] (step=0150320/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0101, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 15:41:06] (step=0150340/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:43:59] (step=0150360/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:46:52] (step=0150380/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:49:45] (step=0150400/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0130, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:52:38] (step=0150420/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 15:55:33] (step=0150440/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0111, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 15:58:28] (step=0150460/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:01:52] (step=0150480/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0077, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 16:04:47] (step=0150500/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:07:42] (step=0150520/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:11:22] (step=0150540/epoch=0009) Train Loss: 0.0066, Gradient Norm: 0.0039, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-24 16:14:15] (step=0150560/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:17:09] (step=0150580/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:20:02] (step=0150600/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:22:56] (step=0150620/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:25:49] (step=0150640/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:28:42] (step=0150660/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:31:36] (step=0150680/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:34:29] (step=0150700/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:37:23] (step=0150720/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:40:17] (step=0150740/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:43:12] (step=0150760/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 16:46:05] (step=0150780/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:48:58] (step=0150800/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:51:52] (step=0150820/epoch=0009) Train Loss: 0.0062, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:54:46] (step=0150840/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 16:57:40] (step=0150860/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:00:34] (step=0150880/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:03:28] (step=0150900/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:06:21] (step=0150920/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:09:16] (step=0150940/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:12:08] (step=0150960/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:15:02] (step=0150980/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:18:13] (step=0151000/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0078, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 17:21:07] (step=0151020/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:24:02] (step=0151040/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:26:55] (step=0151060/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:29:49] (step=0151080/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:32:41] (step=0151100/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:35:35] (step=0151120/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:38:28] (step=0151140/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:41:23] (step=0151160/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:44:16] (step=0151180/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0030, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:47:10] (step=0151200/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:50:04] (step=0151220/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:52:57] (step=0151240/epoch=0009) Train Loss: 0.0111, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 17:55:52] (step=0151260/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 17:58:45] (step=0151280/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:01:38] (step=0151300/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:04:34] (step=0151320/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:07:28] (step=0151340/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:10:21] (step=0151360/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:13:14] (step=0151380/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:16:08] (step=0151400/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:19:02] (step=0151420/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:21:55] (step=0151440/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:25:00] (step=0151460/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:27:54] (step=0151480/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:30:48] (step=0151500/epoch=0009) Train Loss: 0.0111, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:33:40] (step=0151520/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:37:59] (step=0151540/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0041, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-24 18:42:46] (step=0151560/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0080, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-24 18:45:40] (step=0151580/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:48:33] (step=0151600/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 18:51:28] (step=0151620/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:54:22] (step=0151640/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 18:57:16] (step=0151660/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:00:10] (step=0151680/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 19:03:05] (step=0151700/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:06:59] (step=0151720/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0084, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-24 19:11:56] (step=0151740/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0045, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-24 19:14:59] (step=0151760/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:17:52] (step=0151780/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 19:20:46] (step=0151800/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:23:47] (step=0151820/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:26:41] (step=0151840/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:29:36] (step=0151860/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:32:35] (step=0151880/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:35:29] (step=0151900/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:38:22] (step=0151920/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 19:41:16] (step=0151940/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:44:10] (step=0151960/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 19:47:17] (step=0151980/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 19:50:10] (step=0152000/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 19:54:20] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0152000.pt
[2024-10-24 19:57:13] (step=0152020/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0042, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-24 20:00:07] (step=0152040/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:03:01] (step=0152060/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:06:05] (step=0152080/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:08:59] (step=0152100/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:11:54] (step=0152120/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:14:47] (step=0152140/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:18:13] (step=0152160/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0073, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-24 20:21:06] (step=0152180/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:24:00] (step=0152200/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:26:53] (step=0152220/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:29:46] (step=0152240/epoch=0009) Train Loss: 0.0065, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:32:39] (step=0152260/epoch=0009) Train Loss: 0.0104, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:35:33] (step=0152280/epoch=0009) Train Loss: 0.0097, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:38:27] (step=0152300/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:41:20] (step=0152320/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:44:13] (step=0152340/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 20:47:14] (step=0152360/epoch=0009) Train Loss: 0.0101, Gradient Norm: 0.0117, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:50:08] (step=0152380/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:53:10] (step=0152400/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:56:05] (step=0152420/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 20:58:58] (step=0152440/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:01:52] (step=0152460/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:04:46] (step=0152480/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:07:40] (step=0152500/epoch=0009) Train Loss: 0.0108, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:10:35] (step=0152520/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0104, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:13:28] (step=0152540/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:16:23] (step=0152560/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0111, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:19:15] (step=0152580/epoch=0009) Train Loss: 0.0105, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:22:10] (step=0152600/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:25:02] (step=0152620/epoch=0009) Train Loss: 0.0104, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:27:56] (step=0152640/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:30:50] (step=0152660/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:33:42] (step=0152680/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:36:36] (step=0152700/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:39:29] (step=0152720/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:42:23] (step=0152740/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:45:16] (step=0152760/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:48:10] (step=0152780/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:51:04] (step=0152800/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:53:58] (step=0152820/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 21:56:52] (step=0152840/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0095, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 21:59:44] (step=0152860/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:02:39] (step=0152880/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:05:33] (step=0152900/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:08:27] (step=0152920/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:11:19] (step=0152940/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:14:13] (step=0152960/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:17:06] (step=0152980/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:20:00] (step=0153000/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:22:54] (step=0153020/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:25:47] (step=0153040/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:28:41] (step=0153060/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:31:36] (step=0153080/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:34:30] (step=0153100/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:37:23] (step=0153120/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:40:18] (step=0153140/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:43:11] (step=0153160/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0125, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:46:05] (step=0153180/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:48:59] (step=0153200/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:51:52] (step=0153220/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 22:54:46] (step=0153240/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 22:57:40] (step=0153260/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:00:34] (step=0153280/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:03:28] (step=0153300/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:06:23] (step=0153320/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:09:17] (step=0153340/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:12:10] (step=0153360/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:15:05] (step=0153380/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:17:58] (step=0153400/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:21:03] (step=0153420/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:24:10] (step=0153440/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:27:05] (step=0153460/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:29:59] (step=0153480/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:32:53] (step=0153500/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:35:47] (step=0153520/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:38:41] (step=0153540/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:41:36] (step=0153560/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:44:28] (step=0153580/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:47:22] (step=0153600/epoch=0009) Train Loss: 0.0068, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:50:21] (step=0153620/epoch=0009) Train Loss: 0.0064, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:53:16] (step=0153640/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-24 23:56:09] (step=0153660/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-24 23:59:03] (step=0153680/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:01:57] (step=0153700/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:05:02] (step=0153720/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:07:57] (step=0153740/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:10:50] (step=0153760/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:13:44] (step=0153780/epoch=0009) Train Loss: 0.0108, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:16:37] (step=0153800/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:19:32] (step=0153820/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:22:25] (step=0153840/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:25:19] (step=0153860/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:28:13] (step=0153880/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:31:08] (step=0153900/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 00:34:01] (step=0153920/epoch=0009) Train Loss: 0.0112, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:36:55] (step=0153940/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:39:49] (step=0153960/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:42:42] (step=0153980/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:45:35] (step=0154000/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:49:37] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0154000.pt
[2024-10-25 00:52:29] (step=0154020/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0040, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-25 00:55:23] (step=0154040/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 00:58:16] (step=0154060/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:01:08] (step=0154080/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:04:02] (step=0154100/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:07:23] (step=0154120/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0098, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 01:10:23] (step=0154140/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:13:17] (step=0154160/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:16:11] (step=0154180/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:19:03] (step=0154200/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:21:58] (step=0154220/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:25:06] (step=0154240/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:27:58] (step=0154260/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:30:52] (step=0154280/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:33:45] (step=0154300/epoch=0009) Train Loss: 0.0065, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:36:39] (step=0154320/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:39:33] (step=0154340/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:42:31] (step=0154360/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:45:26] (step=0154380/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0034, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:48:19] (step=0154400/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 01:51:14] (step=0154420/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:54:19] (step=0154440/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 01:57:13] (step=0154460/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:00:07] (step=0154480/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0114, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:03:01] (step=0154500/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:05:54] (step=0154520/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:08:48] (step=0154540/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0039, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:11:40] (step=0154560/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0105, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:14:34] (step=0154580/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:17:29] (step=0154600/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:20:21] (step=0154620/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:23:16] (step=0154640/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:26:09] (step=0154660/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:29:04] (step=0154680/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:31:57] (step=0154700/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:34:50] (step=0154720/epoch=0009) Train Loss: 0.0109, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:37:43] (step=0154740/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:40:37] (step=0154760/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:43:31] (step=0154780/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:46:24] (step=0154800/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:49:18] (step=0154820/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0029, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 02:52:12] (step=0154840/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:55:07] (step=0154860/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 02:58:00] (step=0154880/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:07:12] (step=0154900/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0040, Train Steps/Sec: 0.04, lr: 0.000100
[2024-10-25 03:10:05] (step=0154920/epoch=0009) Train Loss: 0.0106, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:12:59] (step=0154940/epoch=0009) Train Loss: 0.0101, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:15:53] (step=0154960/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 03:18:45] (step=0154980/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:21:40] (step=0155000/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 03:24:55] (step=0155020/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0052, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 03:27:48] (step=0155040/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:30:41] (step=0155060/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:33:35] (step=0155080/epoch=0009) Train Loss: 0.0102, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:36:28] (step=0155100/epoch=0009) Train Loss: 0.0102, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:39:22] (step=0155120/epoch=0009) Train Loss: 0.0077, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:42:16] (step=0155140/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:45:10] (step=0155160/epoch=0009) Train Loss: 0.0096, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:48:03] (step=0155180/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:50:56] (step=0155200/epoch=0009) Train Loss: 0.0102, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:53:52] (step=0155220/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 03:56:44] (step=0155240/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 03:59:38] (step=0155260/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:02:31] (step=0155280/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:05:25] (step=0155300/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:08:35] (step=0155320/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0110, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:11:28] (step=0155340/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:14:22] (step=0155360/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:17:15] (step=0155380/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:20:10] (step=0155400/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:24:02] (step=0155420/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0048, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 04:26:57] (step=0155440/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:29:51] (step=0155460/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:32:45] (step=0155480/epoch=0009) Train Loss: 0.0101, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:35:39] (step=0155500/epoch=0009) Train Loss: 0.0103, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:38:32] (step=0155520/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:41:26] (step=0155540/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:44:19] (step=0155560/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:47:27] (step=0155580/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:50:21] (step=0155600/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0116, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:53:15] (step=0155620/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 04:56:08] (step=0155640/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 04:59:03] (step=0155660/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:01:56] (step=0155680/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:04:49] (step=0155700/epoch=0009) Train Loss: 0.0071, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:07:43] (step=0155720/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:10:36] (step=0155740/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:13:30] (step=0155760/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:16:29] (step=0155780/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:19:24] (step=0155800/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:22:16] (step=0155820/epoch=0009) Train Loss: 0.0103, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:25:10] (step=0155840/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:28:04] (step=0155860/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:30:57] (step=0155880/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:33:51] (step=0155900/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:36:45] (step=0155920/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:39:39] (step=0155940/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:42:33] (step=0155960/epoch=0009) Train Loss: 0.0111, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:45:28] (step=0155980/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 05:48:21] (step=0156000/epoch=0009) Train Loss: 0.0070, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 05:52:20] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0156000.pt
[2024-10-25 05:55:14] (step=0156020/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0042, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-25 05:58:08] (step=0156040/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:01:01] (step=0156060/epoch=0009) Train Loss: 0.0081, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:03:55] (step=0156080/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:06:48] (step=0156100/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:09:41] (step=0156120/epoch=0009) Train Loss: 0.0075, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:12:36] (step=0156140/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:15:29] (step=0156160/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:18:23] (step=0156180/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0032, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:21:17] (step=0156200/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:24:12] (step=0156220/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:27:06] (step=0156240/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:30:00] (step=0156260/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:32:54] (step=0156280/epoch=0009) Train Loss: 0.0092, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:35:49] (step=0156300/epoch=0009) Train Loss: 0.0104, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:38:42] (step=0156320/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:41:46] (step=0156340/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:44:39] (step=0156360/epoch=0009) Train Loss: 0.0100, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:47:32] (step=0156380/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:50:27] (step=0156400/epoch=0009) Train Loss: 0.0099, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 06:53:20] (step=0156420/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 06:58:53] (step=0156440/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0088, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-25 07:01:47] (step=0156460/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:04:40] (step=0156480/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:07:33] (step=0156500/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:10:27] (step=0156520/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:13:21] (step=0156540/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:16:14] (step=0156560/epoch=0009) Train Loss: 0.0067, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:19:09] (step=0156580/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:22:02] (step=0156600/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:24:57] (step=0156620/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:27:55] (step=0156640/epoch=0009) Train Loss: 0.0068, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:30:50] (step=0156660/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:33:43] (step=0156680/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:36:38] (step=0156700/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:39:32] (step=0156720/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 07:42:25] (step=0156740/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0034, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:46:09] (step=0156760/epoch=0009) Train Loss: 0.0085, Gradient Norm: 0.0075, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 07:49:02] (step=0156780/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:52:37] (step=0156800/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0071, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 07:55:30] (step=0156820/epoch=0009) Train Loss: 0.0089, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 07:58:25] (step=0156840/epoch=0009) Train Loss: 0.0098, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:01:18] (step=0156860/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:04:13] (step=0156880/epoch=0009) Train Loss: 0.0095, Gradient Norm: 0.0104, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:07:06] (step=0156900/epoch=0009) Train Loss: 0.0088, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:10:00] (step=0156920/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:12:54] (step=0156940/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:15:47] (step=0156960/epoch=0009) Train Loss: 0.0069, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:18:41] (step=0156980/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:21:34] (step=0157000/epoch=0009) Train Loss: 0.0074, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:24:28] (step=0157020/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:27:21] (step=0157040/epoch=0009) Train Loss: 0.0086, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:30:14] (step=0157060/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:33:07] (step=0157080/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:36:00] (step=0157100/epoch=0009) Train Loss: 0.0087, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:38:54] (step=0157120/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:41:48] (step=0157140/epoch=0009) Train Loss: 0.0094, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:44:42] (step=0157160/epoch=0009) Train Loss: 0.0078, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:47:36] (step=0157180/epoch=0009) Train Loss: 0.0076, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:50:30] (step=0157200/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 08:53:23] (step=0157220/epoch=0009) Train Loss: 0.0090, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:56:17] (step=0157240/epoch=0009) Train Loss: 0.0083, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 08:59:11] (step=0157260/epoch=0009) Train Loss: 0.0084, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:02:05] (step=0157280/epoch=0009) Train Loss: 0.0072, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 09:05:01] (step=0157300/epoch=0009) Train Loss: 0.0068, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 09:07:55] (step=0157320/epoch=0009) Train Loss: 0.0093, Gradient Norm: 0.0112, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:11:09] (step=0157340/epoch=0009) Train Loss: 0.0091, Gradient Norm: 0.0061, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 09:14:02] (step=0157360/epoch=0009) Train Loss: 0.0073, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:16:55] (step=0157380/epoch=0009) Train Loss: 0.0082, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:19:48] (step=0157400/epoch=0009) Train Loss: 0.0080, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:22:58] (step=0157420/epoch=0009) Train Loss: 0.0079, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 09:26:49] (step=0157440/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0176, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 09:29:42] (step=0157460/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:32:34] (step=0157480/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0138, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:35:27] (step=0157500/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:38:21] (step=0157520/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0126, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:41:13] (step=0157540/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:44:07] (step=0157560/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:47:00] (step=0157580/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:49:54] (step=0157600/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0120, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 09:52:49] (step=0157620/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 09:57:15] (step=0157640/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0117, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-25 10:00:10] (step=0157660/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 10:03:03] (step=0157680/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0115, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:05:58] (step=0157700/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 10:08:51] (step=0157720/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:11:45] (step=0157740/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:14:38] (step=0157760/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0125, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:17:39] (step=0157780/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 10:20:32] (step=0157800/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:23:26] (step=0157820/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:26:20] (step=0157840/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0114, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 10:29:14] (step=0157860/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 10:32:08] (step=0157880/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:35:02] (step=0157900/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:37:56] (step=0157920/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0111, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 10:40:49] (step=0157940/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:43:42] (step=0157960/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:46:36] (step=0157980/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:49:28] (step=0158000/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 10:53:29] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0158000.pt
[2024-10-25 10:56:23] (step=0158020/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0044, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-25 10:59:34] (step=0158040/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:02:28] (step=0158060/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:05:22] (step=0158080/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0101, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:08:53] (step=0158100/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0044, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 11:11:48] (step=0158120/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0113, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:14:42] (step=0158140/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:17:36] (step=0158160/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:20:30] (step=0158180/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:23:28] (step=0158200/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0111, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:26:21] (step=0158220/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 11:29:14] (step=0158240/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 11:32:18] (step=0158260/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:35:12] (step=0158280/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0121, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:38:04] (step=0158300/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 11:46:39] (step=0158320/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0105, Train Steps/Sec: 0.04, lr: 0.000100
[2024-10-25 11:49:35] (step=0158340/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 11:52:28] (step=0158360/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 11:55:22] (step=0158380/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 11:58:15] (step=0158400/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0112, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:01:09] (step=0158420/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:04:02] (step=0158440/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:06:55] (step=0158460/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:09:49] (step=0158480/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:12:43] (step=0158500/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:15:36] (step=0158520/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:18:29] (step=0158540/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:21:23] (step=0158560/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:24:15] (step=0158580/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:27:09] (step=0158600/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:31:29] (step=0158620/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0046, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-25 12:34:23] (step=0158640/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:37:17] (step=0158660/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:40:24] (step=0158680/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:43:17] (step=0158700/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:46:11] (step=0158720/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:49:06] (step=0158740/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:51:59] (step=0158760/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 12:54:54] (step=0158780/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 12:57:48] (step=0158800/epoch=0010) Train Loss: 0.0059, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:00:41] (step=0158820/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:03:34] (step=0158840/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0100, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:06:29] (step=0158860/epoch=0010) Train Loss: 0.0060, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:09:22] (step=0158880/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:12:15] (step=0158900/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:15:09] (step=0158920/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:18:15] (step=0158940/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:21:10] (step=0158960/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0106, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:24:07] (step=0158980/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:27:00] (step=0159000/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:29:54] (step=0159020/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:32:48] (step=0159040/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:35:42] (step=0159060/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:38:36] (step=0159080/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:41:30] (step=0159100/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:44:24] (step=0159120/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:47:18] (step=0159140/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:50:11] (step=0159160/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:53:06] (step=0159180/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 13:55:59] (step=0159200/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 13:58:52] (step=0159220/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:01:46] (step=0159240/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:04:40] (step=0159260/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:07:44] (step=0159280/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:10:44] (step=0159300/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:13:39] (step=0159320/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:16:32] (step=0159340/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:19:25] (step=0159360/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:22:18] (step=0159380/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:25:12] (step=0159400/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:28:18] (step=0159420/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:32:19] (step=0159440/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0095, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-25 14:35:58] (step=0159460/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0068, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 14:39:14] (step=0159480/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0107, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 14:42:09] (step=0159500/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:45:03] (step=0159520/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:47:58] (step=0159540/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:50:52] (step=0159560/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0097, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:53:53] (step=0159580/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 14:56:47] (step=0159600/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 14:59:40] (step=0159620/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:02:35] (step=0159640/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:05:29] (step=0159660/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:08:24] (step=0159680/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:11:17] (step=0159700/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:14:11] (step=0159720/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:17:04] (step=0159740/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:19:57] (step=0159760/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:22:52] (step=0159780/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:25:45] (step=0159800/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:28:39] (step=0159820/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:31:32] (step=0159840/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 15:34:26] (step=0159860/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:37:21] (step=0159880/epoch=0010) Train Loss: 0.0065, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:40:15] (step=0159900/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:43:23] (step=0159920/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:46:17] (step=0159940/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:49:12] (step=0159960/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:52:07] (step=0159980/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 15:57:34] (step=0160000/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0095, Train Steps/Sec: 0.06, lr: 0.000100
[2024-10-25 16:01:35] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0160000.pt
[2024-10-25 16:05:58] (step=0160020/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0064, Train Steps/Sec: 0.04, lr: 0.000100
[2024-10-25 16:08:52] (step=0160040/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0122, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:11:46] (step=0160060/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:14:40] (step=0160080/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:17:34] (step=0160100/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:20:35] (step=0160120/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:23:29] (step=0160140/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:26:30] (step=0160160/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:29:23] (step=0160180/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:32:28] (step=0160200/epoch=0010) Train Loss: 0.0110, Gradient Norm: 0.0131, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:35:22] (step=0160220/epoch=0010) Train Loss: 0.0098, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:38:16] (step=0160240/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:41:10] (step=0160260/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 16:44:03] (step=0160280/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0112, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:46:56] (step=0160300/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:49:50] (step=0160320/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:52:44] (step=0160340/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 16:57:20] (step=0160360/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0088, Train Steps/Sec: 0.07, lr: 0.000100
[2024-10-25 17:01:24] (step=0160380/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0061, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-25 17:04:42] (step=0160400/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0095, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 17:07:36] (step=0160420/epoch=0010) Train Loss: 0.0111, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:10:30] (step=0160440/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:13:23] (step=0160460/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:16:17] (step=0160480/epoch=0010) Train Loss: 0.0098, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:19:11] (step=0160500/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:22:04] (step=0160520/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:24:59] (step=0160540/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:27:52] (step=0160560/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:30:45] (step=0160580/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:33:38] (step=0160600/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:36:32] (step=0160620/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:39:25] (step=0160640/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:42:19] (step=0160660/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:45:14] (step=0160680/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:48:08] (step=0160700/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:51:01] (step=0160720/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:53:55] (step=0160740/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 17:56:49] (step=0160760/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 17:59:43] (step=0160780/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 18:02:36] (step=0160800/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:05:29] (step=0160820/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:08:25] (step=0160840/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 18:11:18] (step=0160860/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:14:11] (step=0160880/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:17:06] (step=0160900/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 18:20:00] (step=0160920/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:22:53] (step=0160940/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:25:47] (step=0160960/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0098, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:28:40] (step=0160980/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:31:33] (step=0161000/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:34:27] (step=0161020/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:37:20] (step=0161040/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:40:13] (step=0161060/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:43:06] (step=0161080/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:45:59] (step=0161100/epoch=0010) Train Loss: 0.0064, Gradient Norm: 0.0037, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:48:54] (step=0161120/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 18:51:47] (step=0161140/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:54:40] (step=0161160/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 18:57:33] (step=0161180/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:00:27] (step=0161200/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:03:21] (step=0161220/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:06:15] (step=0161240/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:09:10] (step=0161260/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:12:02] (step=0161280/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:14:56] (step=0161300/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:18:01] (step=0161320/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:20:56] (step=0161340/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:23:50] (step=0161360/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:26:44] (step=0161380/epoch=0010) Train Loss: 0.0068, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:29:38] (step=0161400/epoch=0010) Train Loss: 0.0068, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:32:32] (step=0161420/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:35:26] (step=0161440/epoch=0010) Train Loss: 0.0105, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 19:38:19] (step=0161460/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0036, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:41:13] (step=0161480/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:44:07] (step=0161500/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:47:00] (step=0161520/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:49:53] (step=0161540/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:52:46] (step=0161560/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0099, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:55:39] (step=0161580/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 19:58:37] (step=0161600/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 20:01:30] (step=0161620/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:04:23] (step=0161640/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:07:29] (step=0161660/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 20:10:22] (step=0161680/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:13:16] (step=0161700/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:16:09] (step=0161720/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:19:03] (step=0161740/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:21:56] (step=0161760/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:24:50] (step=0161780/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:27:44] (step=0161800/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 20:30:37] (step=0161820/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:33:32] (step=0161840/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 20:36:25] (step=0161860/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:39:19] (step=0161880/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:42:13] (step=0161900/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:45:06] (step=0161920/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:47:59] (step=0161940/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:50:54] (step=0161960/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0110, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 20:53:47] (step=0161980/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 20:56:41] (step=0162000/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0095, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:00:36] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0162000.pt
[2024-10-25 21:03:29] (step=0162020/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0043, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-25 21:06:22] (step=0162040/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0102, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:09:16] (step=0162060/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 21:12:09] (step=0162080/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:15:03] (step=0162100/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:17:56] (step=0162120/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:20:50] (step=0162140/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 21:24:07] (step=0162160/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0088, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 21:27:02] (step=0162180/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 21:29:56] (step=0162200/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 21:32:50] (step=0162220/epoch=0010) Train Loss: 0.0109, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:35:43] (step=0162240/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:38:36] (step=0162260/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:41:30] (step=0162280/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 21:44:42] (step=0162300/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0055, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 21:48:16] (step=0162320/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0092, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-25 21:51:29] (step=0162340/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0061, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-25 21:54:23] (step=0162360/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0105, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 21:57:17] (step=0162380/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:00:10] (step=0162400/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0126, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:03:04] (step=0162420/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:05:57] (step=0162440/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0108, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:08:58] (step=0162460/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:11:51] (step=0162480/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0114, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:14:45] (step=0162500/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:17:40] (step=0162520/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:20:34] (step=0162540/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:23:28] (step=0162560/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:26:22] (step=0162580/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:29:15] (step=0162600/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:32:09] (step=0162620/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:35:03] (step=0162640/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:37:56] (step=0162660/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:40:51] (step=0162680/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:43:44] (step=0162700/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:46:39] (step=0162720/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:49:32] (step=0162740/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 22:52:26] (step=0162760/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:55:21] (step=0162780/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 22:58:16] (step=0162800/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:01:11] (step=0162820/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:04:04] (step=0162840/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:06:58] (step=0162860/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:09:52] (step=0162880/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0106, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:12:47] (step=0162900/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0037, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:15:41] (step=0162920/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0107, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:18:35] (step=0162940/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:21:34] (step=0162960/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:24:28] (step=0162980/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:27:22] (step=0163000/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0107, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:30:16] (step=0163020/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:33:10] (step=0163040/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:36:05] (step=0163060/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:38:58] (step=0163080/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:41:52] (step=0163100/epoch=0010) Train Loss: 0.0098, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:44:45] (step=0163120/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:47:39] (step=0163140/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:50:33] (step=0163160/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:53:27] (step=0163180/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-25 23:56:19] (step=0163200/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0124, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-25 23:59:15] (step=0163220/epoch=0010) Train Loss: 0.0114, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 00:02:08] (step=0163240/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0115, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:05:01] (step=0163260/epoch=0010) Train Loss: 0.0100, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:07:55] (step=0163280/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0121, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:10:47] (step=0163300/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:13:40] (step=0163320/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0154, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:16:33] (step=0163340/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:19:27] (step=0163360/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0137, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 00:22:21] (step=0163380/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 00:25:15] (step=0163400/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0113, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 00:28:11] (step=0163420/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 00:31:04] (step=0163440/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:33:56] (step=0163460/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:36:49] (step=0163480/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0108, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:39:42] (step=0163500/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:42:35] (step=0163520/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:45:29] (step=0163540/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:48:22] (step=0163560/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:51:15] (step=0163580/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:54:08] (step=0163600/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:57:02] (step=0163620/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 00:59:56] (step=0163640/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:02:49] (step=0163660/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0042, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:05:43] (step=0163680/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0077, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:08:42] (step=0163700/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:11:46] (step=0163720/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:14:40] (step=0163740/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:17:35] (step=0163760/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:20:28] (step=0163780/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:23:22] (step=0163800/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:26:16] (step=0163820/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:29:10] (step=0163840/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0122, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:32:04] (step=0163860/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:34:58] (step=0163880/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:37:51] (step=0163900/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:40:45] (step=0163920/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:43:39] (step=0163940/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:46:33] (step=0163960/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 01:49:26] (step=0163980/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:52:20] (step=0164000/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 01:56:12] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0164000.pt
[2024-10-26 01:59:04] (step=0164020/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0045, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-26 02:01:58] (step=0164040/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:04:52] (step=0164060/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:07:46] (step=0164080/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:10:39] (step=0164100/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:13:33] (step=0164120/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0122, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:16:26] (step=0164140/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:19:19] (step=0164160/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:22:14] (step=0164180/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:25:06] (step=0164200/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:28:00] (step=0164220/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:30:53] (step=0164240/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0096, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:33:47] (step=0164260/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:36:39] (step=0164280/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:39:34] (step=0164300/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:42:29] (step=0164320/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0099, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:45:23] (step=0164340/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:48:21] (step=0164360/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:51:14] (step=0164380/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 02:54:13] (step=0164400/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0111, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 02:57:06] (step=0164420/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:00:03] (step=0164440/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:03:50] (step=0164460/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0052, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-26 03:06:56] (step=0164480/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0104, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:09:59] (step=0164500/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:12:53] (step=0164520/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:15:47] (step=0164540/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:18:42] (step=0164560/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:21:37] (step=0164580/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:24:32] (step=0164600/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:27:27] (step=0164620/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:30:20] (step=0164640/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:33:14] (step=0164660/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:36:07] (step=0164680/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0128, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:39:01] (step=0164700/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:41:56] (step=0164720/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:44:49] (step=0164740/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:47:43] (step=0164760/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:50:36] (step=0164780/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 03:53:30] (step=0164800/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:56:34] (step=0164820/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 03:59:28] (step=0164840/epoch=0010) Train Loss: 0.0108, Gradient Norm: 0.0117, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:02:21] (step=0164860/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:05:15] (step=0164880/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0118, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:08:09] (step=0164900/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:12:05] (step=0164920/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0102, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-26 04:14:59] (step=0164940/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:17:54] (step=0164960/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0145, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:20:48] (step=0164980/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:23:42] (step=0165000/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:26:35] (step=0165020/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:29:30] (step=0165040/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:32:23] (step=0165060/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:35:17] (step=0165080/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:38:11] (step=0165100/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:41:05] (step=0165120/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:43:58] (step=0165140/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:46:53] (step=0165160/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:49:47] (step=0165180/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 04:52:41] (step=0165200/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0120, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:55:36] (step=0165220/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 04:58:49] (step=0165240/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0118, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-26 05:01:43] (step=0165260/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:04:37] (step=0165280/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:07:31] (step=0165300/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:10:24] (step=0165320/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0110, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:13:18] (step=0165340/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:16:11] (step=0165360/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:19:05] (step=0165380/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:21:59] (step=0165400/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0104, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:24:52] (step=0165420/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:27:47] (step=0165440/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0099, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:30:40] (step=0165460/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:33:35] (step=0165480/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0101, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:36:28] (step=0165500/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:39:38] (step=0165520/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:42:32] (step=0165540/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:45:25] (step=0165560/epoch=0010) Train Loss: 0.0098, Gradient Norm: 0.0105, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:48:19] (step=0165580/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:51:14] (step=0165600/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0112, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:54:08] (step=0165620/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 05:57:01] (step=0165640/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 05:59:56] (step=0165660/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 06:02:50] (step=0165680/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:05:44] (step=0165700/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 06:08:38] (step=0165720/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:11:32] (step=0165740/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:14:25] (step=0165760/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0108, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:17:18] (step=0165780/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:20:12] (step=0165800/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:23:06] (step=0165820/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:25:59] (step=0165840/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:28:52] (step=0165860/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:31:47] (step=0165880/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0095, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 06:34:40] (step=0165900/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:37:34] (step=0165920/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0099, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:40:27] (step=0165940/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:43:20] (step=0165960/epoch=0010) Train Loss: 0.0064, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:46:13] (step=0165980/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:49:07] (step=0166000/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 06:53:05] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0166000.pt
[2024-10-26 06:55:57] (step=0166020/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0040, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-26 06:58:50] (step=0166040/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0112, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:01:45] (step=0166060/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:04:37] (step=0166080/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:07:31] (step=0166100/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:10:25] (step=0166120/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:13:20] (step=0166140/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0036, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:16:14] (step=0166160/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0095, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:19:07] (step=0166180/epoch=0010) Train Loss: 0.0111, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:22:00] (step=0166200/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0117, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:24:53] (step=0166220/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:27:47] (step=0166240/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:30:40] (step=0166260/epoch=0010) Train Loss: 0.0098, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:33:35] (step=0166280/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:36:28] (step=0166300/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:39:21] (step=0166320/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0072, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:42:15] (step=0166340/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:45:08] (step=0166360/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:48:02] (step=0166380/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:50:56] (step=0166400/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0107, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:54:03] (step=0166420/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 07:56:57] (step=0166440/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 07:59:52] (step=0166460/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0185, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:02:45] (step=0166480/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:05:39] (step=0166500/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:08:33] (step=0166520/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0126, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:11:27] (step=0166540/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:14:21] (step=0166560/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:17:14] (step=0166580/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:20:09] (step=0166600/epoch=0010) Train Loss: 0.0098, Gradient Norm: 0.0175, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:23:02] (step=0166620/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:25:57] (step=0166640/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0133, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:29:06] (step=0166660/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:31:58] (step=0166680/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:34:52] (step=0166700/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:37:45] (step=0166720/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0139, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:40:39] (step=0166740/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:43:32] (step=0166760/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0095, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:46:26] (step=0166780/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:49:19] (step=0166800/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:52:13] (step=0166820/epoch=0010) Train Loss: 0.0100, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 08:55:08] (step=0166840/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 08:58:02] (step=0166860/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:00:55] (step=0166880/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0104, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:03:48] (step=0166900/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:06:42] (step=0166920/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0127, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:09:36] (step=0166940/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:12:30] (step=0166960/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0105, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:15:23] (step=0166980/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:18:17] (step=0167000/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0117, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:21:11] (step=0167020/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:24:05] (step=0167040/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:26:58] (step=0167060/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:29:51] (step=0167080/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:32:45] (step=0167100/epoch=0010) Train Loss: 0.0068, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:35:39] (step=0167120/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:38:34] (step=0167140/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:41:26] (step=0167160/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0086, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:44:22] (step=0167180/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:47:16] (step=0167200/epoch=0010) Train Loss: 0.0108, Gradient Norm: 0.0141, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:50:10] (step=0167220/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:53:05] (step=0167240/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 09:55:58] (step=0167260/epoch=0010) Train Loss: 0.0066, Gradient Norm: 0.0041, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 09:58:53] (step=0167280/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:01:46] (step=0167300/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:04:39] (step=0167320/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:07:32] (step=0167340/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:10:27] (step=0167360/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:13:21] (step=0167380/epoch=0010) Train Loss: 0.0108, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:16:14] (step=0167400/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:19:08] (step=0167420/epoch=0010) Train Loss: 0.0105, Gradient Norm: 0.0060, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:22:16] (step=0167440/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:25:10] (step=0167460/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:28:04] (step=0167480/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:30:58] (step=0167500/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:33:51] (step=0167520/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0092, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:36:44] (step=0167540/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:39:38] (step=0167560/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:42:31] (step=0167580/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:45:25] (step=0167600/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:48:18] (step=0167620/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:51:12] (step=0167640/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:54:05] (step=0167660/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 10:57:00] (step=0167680/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0114, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 10:59:54] (step=0167700/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:02:47] (step=0167720/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:05:41] (step=0167740/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:08:35] (step=0167760/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:11:29] (step=0167780/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:14:30] (step=0167800/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:17:26] (step=0167820/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:20:19] (step=0167840/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:23:13] (step=0167860/epoch=0010) Train Loss: 0.0112, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:26:06] (step=0167880/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0115, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:29:00] (step=0167900/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:31:54] (step=0167920/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0113, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:34:47] (step=0167940/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:37:41] (step=0167960/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:40:34] (step=0167980/epoch=0010) Train Loss: 0.0067, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:43:28] (step=0168000/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 11:47:21] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0168000.pt
[2024-10-26 11:50:13] (step=0168020/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0041, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-26 11:53:07] (step=0168040/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:56:02] (step=0168060/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 11:58:56] (step=0168080/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:01:49] (step=0168100/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:04:42] (step=0168120/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:07:35] (step=0168140/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:10:28] (step=0168160/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:13:22] (step=0168180/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:16:16] (step=0168200/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:19:10] (step=0168220/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:22:03] (step=0168240/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0097, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:24:58] (step=0168260/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:27:52] (step=0168280/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:30:46] (step=0168300/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:33:40] (step=0168320/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:36:33] (step=0168340/epoch=0010) Train Loss: 0.0124, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:39:27] (step=0168360/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:42:20] (step=0168380/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:45:13] (step=0168400/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:48:05] (step=0168420/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:50:59] (step=0168440/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0133, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:53:53] (step=0168460/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 12:56:46] (step=0168480/epoch=0010) Train Loss: 0.0060, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 12:59:40] (step=0168500/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:02:33] (step=0168520/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0084, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:05:27] (step=0168540/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:09:42] (step=0168560/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0064, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-26 13:12:36] (step=0168580/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:15:30] (step=0168600/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:18:24] (step=0168620/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:21:19] (step=0168640/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:24:13] (step=0168660/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:27:07] (step=0168680/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:30:00] (step=0168700/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:32:54] (step=0168720/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:35:48] (step=0168740/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:38:43] (step=0168760/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0121, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:41:36] (step=0168780/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:44:29] (step=0168800/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:47:23] (step=0168820/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:50:17] (step=0168840/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 13:53:11] (step=0168860/epoch=0010) Train Loss: 0.0100, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:56:05] (step=0168880/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 13:59:00] (step=0168900/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:01:54] (step=0168920/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:04:49] (step=0168940/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:07:42] (step=0168960/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:10:57] (step=0168980/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0050, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-26 14:13:52] (step=0169000/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:16:45] (step=0169020/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0079, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:19:39] (step=0169040/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0130, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:22:32] (step=0169060/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:25:27] (step=0169080/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:28:21] (step=0169100/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:31:15] (step=0169120/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:34:08] (step=0169140/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:37:03] (step=0169160/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0100, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:39:58] (step=0169180/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 14:42:51] (step=0169200/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:45:44] (step=0169220/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:48:38] (step=0169240/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0155, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:51:31] (step=0169260/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:54:24] (step=0169280/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0147, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 14:57:18] (step=0169300/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:00:12] (step=0169320/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0121, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:03:07] (step=0169340/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:06:02] (step=0169360/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0141, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:08:55] (step=0169380/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:11:49] (step=0169400/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:14:42] (step=0169420/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:17:37] (step=0169440/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:20:30] (step=0169460/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:23:24] (step=0169480/epoch=0010) Train Loss: 0.0068, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:26:17] (step=0169500/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0038, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:29:11] (step=0169520/epoch=0010) Train Loss: 0.0066, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:32:05] (step=0169540/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:35:19] (step=0169560/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0103, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-26 15:38:13] (step=0169580/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:41:06] (step=0169600/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:44:00] (step=0169620/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:46:53] (step=0169640/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:49:47] (step=0169660/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:52:40] (step=0169680/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0109, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 15:55:34] (step=0169700/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 15:58:29] (step=0169720/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 16:01:22] (step=0169740/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:04:16] (step=0169760/epoch=0010) Train Loss: 0.0065, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 16:07:10] (step=0169780/epoch=0010) Train Loss: 0.0100, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:10:03] (step=0169800/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0101, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:12:57] (step=0169820/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:15:50] (step=0169840/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0099, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:18:44] (step=0169860/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0046, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:21:37] (step=0169880/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0082, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:24:31] (step=0169900/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:27:24] (step=0169920/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:30:18] (step=0169940/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:33:52] (step=0169960/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0079, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-26 16:36:46] (step=0169980/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 16:39:41] (step=0170000/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 16:43:35] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0170000.pt
[2024-10-26 16:46:29] (step=0170020/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0050, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-26 16:49:22] (step=0170040/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0108, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 16:52:16] (step=0170060/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 16:55:11] (step=0170080/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 16:58:04] (step=0170100/epoch=0010) Train Loss: 0.0072, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:00:58] (step=0170120/epoch=0010) Train Loss: 0.0100, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:03:51] (step=0170140/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:06:45] (step=0170160/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:09:38] (step=0170180/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:13:06] (step=0170200/epoch=0010) Train Loss: 0.0066, Gradient Norm: 0.0076, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-26 17:16:00] (step=0170220/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:18:54] (step=0170240/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:21:49] (step=0170260/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:24:43] (step=0170280/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:27:37] (step=0170300/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:30:31] (step=0170320/epoch=0010) Train Loss: 0.0104, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:33:24] (step=0170340/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0045, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:36:18] (step=0170360/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0106, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:39:12] (step=0170380/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:42:06] (step=0170400/epoch=0010) Train Loss: 0.0074, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:45:00] (step=0170420/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0040, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:47:54] (step=0170440/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:50:47] (step=0170460/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:53:42] (step=0170480/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 17:56:34] (step=0170500/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 17:59:28] (step=0170520/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:02:23] (step=0170540/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0039, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:05:16] (step=0170560/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0121, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:08:10] (step=0170580/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:11:10] (step=0170600/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0099, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:14:04] (step=0170620/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:16:57] (step=0170640/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:19:51] (step=0170660/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:22:44] (step=0170680/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0123, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:25:39] (step=0170700/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:28:32] (step=0170720/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0085, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:31:26] (step=0170740/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:34:21] (step=0170760/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0107, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:37:15] (step=0170780/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:40:09] (step=0170800/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0118, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:43:03] (step=0170820/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:45:56] (step=0170840/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:48:50] (step=0170860/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 18:51:44] (step=0170880/epoch=0010) Train Loss: 0.0061, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:54:37] (step=0170900/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 18:57:33] (step=0170920/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:00:27] (step=0170940/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:03:22] (step=0170960/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0113, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:06:15] (step=0170980/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 19:09:09] (step=0171000/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0117, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 19:12:03] (step=0171020/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:14:58] (step=0171040/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:17:53] (step=0171060/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:20:46] (step=0171080/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 19:23:40] (step=0171100/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:26:36] (step=0171120/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0093, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:29:30] (step=0171140/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:32:26] (step=0171160/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:35:20] (step=0171180/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:38:15] (step=0171200/epoch=0010) Train Loss: 0.0111, Gradient Norm: 0.0110, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:41:08] (step=0171220/epoch=0010) Train Loss: 0.0104, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 19:44:03] (step=0171240/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:46:57] (step=0171260/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:49:52] (step=0171280/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0096, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:52:47] (step=0171300/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:55:42] (step=0171320/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 19:58:36] (step=0171340/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:01:30] (step=0171360/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 20:04:26] (step=0171380/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:07:21] (step=0171400/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0123, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:10:15] (step=0171420/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0053, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 20:13:28] (step=0171440/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0115, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-26 20:16:22] (step=0171460/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 20:19:15] (step=0171480/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 20:22:10] (step=0171500/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:25:05] (step=0171520/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:28:00] (step=0171540/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:30:55] (step=0171560/epoch=0010) Train Loss: 0.0071, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:33:49] (step=0171580/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:36:46] (step=0171600/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:39:39] (step=0171620/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 20:42:33] (step=0171640/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:45:29] (step=0171660/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:48:24] (step=0171680/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0129, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:51:19] (step=0171700/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:54:13] (step=0171720/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 20:57:07] (step=0171740/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:00:02] (step=0171760/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0108, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:02:56] (step=0171780/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:05:52] (step=0171800/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:09:30] (step=0171820/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0060, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-26 21:12:24] (step=0171840/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0132, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:15:19] (step=0171860/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:18:12] (step=0171880/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 21:21:16] (step=0171900/epoch=0010) Train Loss: 0.0086, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:24:10] (step=0171920/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0089, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:27:07] (step=0171940/epoch=0010) Train Loss: 0.0075, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:30:01] (step=0171960/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0109, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:32:54] (step=0171980/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 21:35:49] (step=0172000/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:39:35] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0172000.pt
[2024-10-26 21:42:28] (step=0172020/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0041, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-26 21:45:22] (step=0172040/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0076, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 21:48:17] (step=0172060/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0040, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:51:12] (step=0172080/epoch=0010) Train Loss: 0.0085, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:54:07] (step=0172100/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 21:57:00] (step=0172120/epoch=0010) Train Loss: 0.0093, Gradient Norm: 0.0094, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 21:59:55] (step=0172140/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:02:55] (step=0172160/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0102, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:05:54] (step=0172180/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:08:48] (step=0172200/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0091, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:11:41] (step=0172220/epoch=0010) Train Loss: 0.0103, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:14:36] (step=0172240/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0098, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:17:29] (step=0172260/epoch=0010) Train Loss: 0.0094, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:20:23] (step=0172280/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:23:14] (step=0172300/epoch=0010) Train Loss: 0.0090, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:26:08] (step=0172320/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:29:02] (step=0172340/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:31:55] (step=0172360/epoch=0010) Train Loss: 0.0096, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:34:49] (step=0172380/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:37:43] (step=0172400/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0075, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:40:37] (step=0172420/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:43:31] (step=0172440/epoch=0010) Train Loss: 0.0101, Gradient Norm: 0.0117, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:46:25] (step=0172460/epoch=0010) Train Loss: 0.0070, Gradient Norm: 0.0047, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:49:18] (step=0172480/epoch=0010) Train Loss: 0.0079, Gradient Norm: 0.0117, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 22:52:12] (step=0172500/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:55:06] (step=0172520/epoch=0010) Train Loss: 0.0065, Gradient Norm: 0.0077, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 22:58:11] (step=0172540/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:01:04] (step=0172560/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0111, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 23:03:59] (step=0172580/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:06:54] (step=0172600/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0105, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:09:48] (step=0172620/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:12:43] (step=0172640/epoch=0010) Train Loss: 0.0084, Gradient Norm: 0.0091, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:15:37] (step=0172660/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0035, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-26 23:18:31] (step=0172680/epoch=0010) Train Loss: 0.0097, Gradient Norm: 0.0087, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:21:26] (step=0172700/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:24:21] (step=0172720/epoch=0010) Train Loss: 0.0066, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:27:16] (step=0172740/epoch=0010) Train Loss: 0.0087, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:30:10] (step=0172760/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0072, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:33:04] (step=0172780/epoch=0010) Train Loss: 0.0077, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:36:00] (step=0172800/epoch=0010) Train Loss: 0.0089, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:38:56] (step=0172820/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:41:51] (step=0172840/epoch=0010) Train Loss: 0.0080, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:44:46] (step=0172860/epoch=0010) Train Loss: 0.0082, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:47:41] (step=0172880/epoch=0010) Train Loss: 0.0076, Gradient Norm: 0.0094, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:50:36] (step=0172900/epoch=0010) Train Loss: 0.0069, Gradient Norm: 0.0038, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:53:30] (step=0172920/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-26 23:57:52] (step=0172940/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0038, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-27 00:00:47] (step=0172960/epoch=0010) Train Loss: 0.0081, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:03:42] (step=0172980/epoch=0010) Train Loss: 0.0091, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:06:38] (step=0173000/epoch=0010) Train Loss: 0.0078, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:09:32] (step=0173020/epoch=0010) Train Loss: 0.0092, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:12:27] (step=0173040/epoch=0010) Train Loss: 0.0099, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:15:22] (step=0173060/epoch=0010) Train Loss: 0.0095, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:18:18] (step=0173080/epoch=0010) Train Loss: 0.0073, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:21:13] (step=0173100/epoch=0010) Train Loss: 0.0088, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:24:08] (step=0173120/epoch=0010) Train Loss: 0.0106, Gradient Norm: 0.0118, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:27:03] (step=0173140/epoch=0010) Train Loss: 0.0083, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:29:57] (step=0173160/epoch=0010) Train Loss: 0.0102, Gradient Norm: 0.0114, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:36:07] (step=0173180/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0157, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-27 00:39:00] (step=0173200/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0018, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 00:41:53] (step=0173220/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 00:44:46] (step=0173240/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 00:47:41] (step=0173260/epoch=0011) Train Loss: 0.0111, Gradient Norm: 0.0092, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:50:35] (step=0173280/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 00:53:46] (step=0173300/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0084, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 00:56:41] (step=0173320/epoch=0011) Train Loss: 0.0098, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 00:59:34] (step=0173340/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 01:02:29] (step=0173360/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:05:24] (step=0173380/epoch=0011) Train Loss: 0.0069, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:08:20] (step=0173400/epoch=0011) Train Loss: 0.0101, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:11:14] (step=0173420/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:14:09] (step=0173440/epoch=0011) Train Loss: 0.0071, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:17:04] (step=0173460/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:19:59] (step=0173480/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:22:54] (step=0173500/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0090, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:25:49] (step=0173520/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:28:44] (step=0173540/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:31:39] (step=0173560/epoch=0011) Train Loss: 0.0063, Gradient Norm: 0.0011, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:34:34] (step=0173580/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:37:29] (step=0173600/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:40:23] (step=0173620/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:43:19] (step=0173640/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:46:14] (step=0173660/epoch=0011) Train Loss: 0.0096, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:49:09] (step=0173680/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:52:03] (step=0173700/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:54:58] (step=0173720/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 01:57:53] (step=0173740/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:00:48] (step=0173760/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:03:42] (step=0173780/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:06:37] (step=0173800/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:09:31] (step=0173820/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:12:36] (step=0173840/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0007, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:15:30] (step=0173860/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:18:24] (step=0173880/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:21:18] (step=0173900/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:24:13] (step=0173920/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:27:07] (step=0173940/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:30:02] (step=0173960/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:32:58] (step=0173980/epoch=0011) Train Loss: 0.0070, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:35:53] (step=0174000/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:39:42] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0174000.pt
[2024-10-27 02:42:37] (step=0174020/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0071, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-27 02:45:31] (step=0174040/epoch=0011) Train Loss: 0.0071, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:48:25] (step=0174060/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:51:20] (step=0174080/epoch=0011) Train Loss: 0.0061, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:54:15] (step=0174100/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 02:57:10] (step=0174120/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:00:04] (step=0174140/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:03:00] (step=0174160/epoch=0011) Train Loss: 0.0071, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:05:54] (step=0174180/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:08:51] (step=0174200/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:11:45] (step=0174220/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0044, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:14:40] (step=0174240/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:17:36] (step=0174260/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:20:30] (step=0174280/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:23:26] (step=0174300/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:26:42] (step=0174320/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0015, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 03:29:36] (step=0174340/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0071, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:32:31] (step=0174360/epoch=0011) Train Loss: 0.0071, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:35:25] (step=0174380/epoch=0011) Train Loss: 0.0069, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:38:21] (step=0174400/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:41:15] (step=0174420/epoch=0011) Train Loss: 0.0070, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:44:10] (step=0174440/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:47:05] (step=0174460/epoch=0011) Train Loss: 0.0070, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:50:01] (step=0174480/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 03:54:16] (step=0174500/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0062, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-27 03:57:12] (step=0174520/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:00:08] (step=0174540/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:03:59] (step=0174560/epoch=0011) Train Loss: 0.0072, Gradient Norm: 0.0013, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-27 04:06:53] (step=0174580/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:09:47] (step=0174600/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0029, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:12:42] (step=0174620/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:15:38] (step=0174640/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:18:31] (step=0174660/epoch=0011) Train Loss: 0.0099, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 04:21:26] (step=0174680/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:24:20] (step=0174700/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:27:15] (step=0174720/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:30:09] (step=0174740/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0067, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 04:33:04] (step=0174760/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0011, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:35:57] (step=0174780/epoch=0011) Train Loss: 0.0098, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 04:38:52] (step=0174800/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:41:47] (step=0174820/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:44:42] (step=0174840/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:47:36] (step=0174860/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:50:30] (step=0174880/epoch=0011) Train Loss: 0.0099, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:53:25] (step=0174900/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:56:20] (step=0174920/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0033, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 04:59:15] (step=0174940/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:02:10] (step=0174960/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:05:04] (step=0174980/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:07:58] (step=0175000/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:10:52] (step=0175020/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:13:47] (step=0175040/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:16:42] (step=0175060/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:19:38] (step=0175080/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:22:33] (step=0175100/epoch=0011) Train Loss: 0.0096, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:25:28] (step=0175120/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:28:22] (step=0175140/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:31:16] (step=0175160/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:34:11] (step=0175180/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0057, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:37:06] (step=0175200/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:40:01] (step=0175220/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0042, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:42:59] (step=0175240/epoch=0011) Train Loss: 0.0064, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:45:53] (step=0175260/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0078, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:48:48] (step=0175280/epoch=0011) Train Loss: 0.0104, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:51:44] (step=0175300/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:54:39] (step=0175320/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 05:57:33] (step=0175340/epoch=0011) Train Loss: 0.0069, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:00:27] (step=0175360/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0028, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 06:03:22] (step=0175380/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0088, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:06:16] (step=0175400/epoch=0011) Train Loss: 0.0098, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:09:10] (step=0175420/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:12:32] (step=0175440/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0015, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 06:15:27] (step=0175460/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:18:21] (step=0175480/epoch=0011) Train Loss: 0.0065, Gradient Norm: 0.0010, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:21:16] (step=0175500/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:24:11] (step=0175520/epoch=0011) Train Loss: 0.0065, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:27:06] (step=0175540/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:29:59] (step=0175560/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0013, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 06:32:53] (step=0175580/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0052, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 06:35:48] (step=0175600/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0010, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:38:42] (step=0175620/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:42:51] (step=0175640/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0026, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-27 06:45:45] (step=0175660/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0082, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:48:38] (step=0175680/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 06:51:32] (step=0175700/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:54:27] (step=0175720/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 06:57:21] (step=0175740/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:00:15] (step=0175760/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:03:09] (step=0175780/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:06:03] (step=0175800/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:08:57] (step=0175820/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:11:53] (step=0175840/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:14:47] (step=0175860/epoch=0011) Train Loss: 0.0102, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:17:49] (step=0175880/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:20:44] (step=0175900/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:23:38] (step=0175920/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:26:32] (step=0175940/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:29:27] (step=0175960/epoch=0011) Train Loss: 0.0096, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:32:21] (step=0175980/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 07:35:16] (step=0176000/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:39:10] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0176000.pt
[2024-10-27 07:42:04] (step=0176020/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0059, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-27 07:44:58] (step=0176040/epoch=0011) Train Loss: 0.0102, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:47:52] (step=0176060/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0051, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 07:50:47] (step=0176080/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0011, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:53:42] (step=0176100/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:56:37] (step=0176120/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 07:59:31] (step=0176140/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0045, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:02:25] (step=0176160/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:05:20] (step=0176180/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:08:49] (step=0176200/epoch=0011) Train Loss: 0.0096, Gradient Norm: 0.0028, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 08:11:43] (step=0176220/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0081, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:14:39] (step=0176240/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:17:34] (step=0176260/epoch=0011) Train Loss: 0.0106, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:20:29] (step=0176280/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:23:23] (step=0176300/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:26:17] (step=0176320/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:29:11] (step=0176340/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:32:06] (step=0176360/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:35:01] (step=0176380/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:37:55] (step=0176400/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:40:48] (step=0176420/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 08:43:42] (step=0176440/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 08:46:56] (step=0176460/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0043, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 08:49:49] (step=0176480/epoch=0011) Train Loss: 0.0072, Gradient Norm: 0.0011, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 08:52:49] (step=0176500/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0051, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 08:56:09] (step=0176520/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0018, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 08:59:04] (step=0176540/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:01:58] (step=0176560/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:04:53] (step=0176580/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:07:47] (step=0176600/epoch=0011) Train Loss: 0.0096, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:10:40] (step=0176620/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 09:13:34] (step=0176640/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 09:16:27] (step=0176660/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0044, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 09:19:21] (step=0176680/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:22:15] (step=0176700/epoch=0011) Train Loss: 0.0099, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:25:11] (step=0176720/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0011, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:28:05] (step=0176740/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:30:59] (step=0176760/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:33:54] (step=0176780/epoch=0011) Train Loss: 0.0065, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:36:48] (step=0176800/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:39:43] (step=0176820/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:42:37] (step=0176840/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0043, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 09:45:30] (step=0176860/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0099, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 09:48:24] (step=0176880/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:51:18] (step=0176900/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:54:13] (step=0176920/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 09:57:07] (step=0176940/epoch=0011) Train Loss: 0.0103, Gradient Norm: 0.0089, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 10:00:01] (step=0176960/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:02:55] (step=0176980/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 10:05:49] (step=0177000/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0014, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 10:08:43] (step=0177020/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 10:11:37] (step=0177040/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:14:31] (step=0177060/epoch=0011) Train Loss: 0.0069, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:17:25] (step=0177080/epoch=0011) Train Loss: 0.0071, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:20:19] (step=0177100/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:23:13] (step=0177120/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:26:10] (step=0177140/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0063, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:29:10] (step=0177160/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:32:21] (step=0177180/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0064, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 10:35:15] (step=0177200/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:38:10] (step=0177220/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0041, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:41:05] (step=0177240/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:43:59] (step=0177260/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:46:53] (step=0177280/epoch=0011) Train Loss: 0.0109, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 10:49:48] (step=0177300/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:52:42] (step=0177320/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 10:55:36] (step=0177340/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0074, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 10:58:29] (step=0177360/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:01:23] (step=0177380/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:04:17] (step=0177400/epoch=0011) Train Loss: 0.0098, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:07:10] (step=0177420/epoch=0011) Train Loss: 0.0108, Gradient Norm: 0.0103, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:10:05] (step=0177440/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0028, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:13:00] (step=0177460/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:15:56] (step=0177480/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:18:50] (step=0177500/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0067, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:21:43] (step=0177520/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0014, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:24:37] (step=0177540/epoch=0011) Train Loss: 0.0113, Gradient Norm: 0.0069, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:27:30] (step=0177560/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:30:24] (step=0177580/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0064, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:33:17] (step=0177600/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0014, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:36:11] (step=0177620/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:39:35] (step=0177640/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0025, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 11:42:28] (step=0177660/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0050, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:45:23] (step=0177680/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 11:49:26] (step=0177700/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0063, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-27 11:52:20] (step=0177720/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:55:14] (step=0177740/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 11:58:08] (step=0177760/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:01:01] (step=0177780/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0063, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:03:56] (step=0177800/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:06:50] (step=0177820/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:09:44] (step=0177840/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:12:38] (step=0177860/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:15:32] (step=0177880/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:18:26] (step=0177900/epoch=0011) Train Loss: 0.0108, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:21:20] (step=0177920/epoch=0011) Train Loss: 0.0101, Gradient Norm: 0.0014, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:24:14] (step=0177940/epoch=0011) Train Loss: 0.0066, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:27:07] (step=0177960/epoch=0011) Train Loss: 0.0113, Gradient Norm: 0.0027, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:30:02] (step=0177980/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:32:56] (step=0178000/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0031, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:36:45] Saved checkpoint to ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0178000.pt
[2024-10-27 12:39:38] (step=0178020/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0092, Train Steps/Sec: 0.05, lr: 0.000100
[2024-10-27 12:42:32] (step=0178040/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:45:26] (step=0178060/epoch=0011) Train Loss: 0.0096, Gradient Norm: 0.0084, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:48:21] (step=0178080/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 12:51:13] (step=0178100/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:54:07] (step=0178120/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0016, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:57:01] (step=0178140/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 12:59:55] (step=0178160/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:02:55] (step=0178180/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0069, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:05:49] (step=0178200/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:08:43] (step=0178220/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:11:37] (step=0178240/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:14:31] (step=0178260/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0081, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:17:26] (step=0178280/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0012, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:20:45] (step=0178300/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0053, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 13:23:40] (step=0178320/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:26:34] (step=0178340/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:29:28] (step=0178360/epoch=0011) Train Loss: 0.0072, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:32:22] (step=0178380/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:35:15] (step=0178400/epoch=0011) Train Loss: 0.0066, Gradient Norm: 0.0011, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:38:09] (step=0178420/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:41:03] (step=0178440/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:44:06] (step=0178460/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0075, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:47:07] (step=0178480/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:50:01] (step=0178500/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0071, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:52:55] (step=0178520/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0016, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 13:55:49] (step=0178540/epoch=0011) Train Loss: 0.0101, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 13:58:42] (step=0178560/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:01:37] (step=0178580/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:04:32] (step=0178600/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:07:26] (step=0178620/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0043, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:10:19] (step=0178640/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0032, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:13:13] (step=0178660/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0046, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:16:06] (step=0178680/epoch=0011) Train Loss: 0.0101, Gradient Norm: 0.0016, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:19:01] (step=0178700/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:21:56] (step=0178720/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:24:50] (step=0178740/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0061, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:27:44] (step=0178760/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:30:39] (step=0178780/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:33:33] (step=0178800/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:36:27] (step=0178820/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0088, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:39:21] (step=0178840/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:42:16] (step=0178860/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0070, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:45:10] (step=0178880/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:48:04] (step=0178900/epoch=0011) Train Loss: 0.0067, Gradient Norm: 0.0066, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:50:57] (step=0178920/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:54:02] (step=0178940/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 14:56:55] (step=0178960/epoch=0011) Train Loss: 0.0072, Gradient Norm: 0.0019, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 14:59:49] (step=0178980/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:02:43] (step=0179000/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:05:38] (step=0179020/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0049, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:08:32] (step=0179040/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0031, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:11:27] (step=0179060/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:14:21] (step=0179080/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:17:14] (step=0179100/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:20:08] (step=0179120/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:23:02] (step=0179140/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0048, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:25:55] (step=0179160/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:28:50] (step=0179180/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:31:45] (step=0179200/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:34:39] (step=0179220/epoch=0011) Train Loss: 0.0064, Gradient Norm: 0.0048, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:37:35] (step=0179240/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:40:29] (step=0179260/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0057, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:43:23] (step=0179280/epoch=0011) Train Loss: 0.0072, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:46:17] (step=0179300/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:49:11] (step=0179320/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0011, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 15:52:05] (step=0179340/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0059, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:54:59] (step=0179360/epoch=0011) Train Loss: 0.0098, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 15:57:53] (step=0179380/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:00:46] (step=0179400/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0014, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:03:39] (step=0179420/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0073, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:06:32] (step=0179440/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0020, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:09:25] (step=0179460/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:12:18] (step=0179480/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0018, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:15:12] (step=0179500/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:18:06] (step=0179520/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0025, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:21:00] (step=0179540/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:23:54] (step=0179560/epoch=0011) Train Loss: 0.0066, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:26:48] (step=0179580/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:29:42] (step=0179600/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:32:37] (step=0179620/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:35:31] (step=0179640/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:38:26] (step=0179660/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:41:19] (step=0179680/epoch=0011) Train Loss: 0.0098, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:44:14] (step=0179700/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0064, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:47:08] (step=0179720/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:50:03] (step=0179740/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:52:57] (step=0179760/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0033, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 16:55:51] (step=0179780/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0068, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 16:58:46] (step=0179800/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:01:39] (step=0179820/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0090, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:04:33] (step=0179840/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:07:26] (step=0179860/epoch=0011) Train Loss: 0.0067, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:10:21] (step=0179880/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:13:16] (step=0179900/epoch=0011) Train Loss: 0.0061, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:16:10] (step=0179920/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:19:04] (step=0179940/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0087, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:21:59] (step=0179960/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:25:03] (step=0179980/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0083, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:27:57] (step=0180000/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0014, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:28:31] Error when saving at ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0180000.pt
[2024-10-27 17:29:16] Changing to saved checkpoint to ./results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0180000.pt
[2024-10-27 17:32:10] (step=0180020/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0052, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-27 17:35:05] (step=0180040/epoch=0011) Train Loss: 0.0072, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:37:59] (step=0180060/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0054, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:40:53] (step=0180080/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:43:48] (step=0180100/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:46:40] (step=0180120/epoch=0011) Train Loss: 0.0067, Gradient Norm: 0.0013, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:49:34] (step=0180140/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:52:28] (step=0180160/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 17:55:21] (step=0180180/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 17:58:16] (step=0180200/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:01:12] (step=0180220/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:04:07] (step=0180240/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:07:03] (step=0180260/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:09:57] (step=0180280/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0016, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:12:50] (step=0180300/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0054, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 18:15:44] (step=0180320/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:18:38] (step=0180340/epoch=0011) Train Loss: 0.0074, Gradient Norm: 0.0052, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:21:32] (step=0180360/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0010, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 18:24:26] (step=0180380/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0080, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:27:20] (step=0180400/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 18:30:15] (step=0180420/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0065, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:33:09] (step=0180440/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:36:04] (step=0180460/epoch=0011) Train Loss: 0.0073, Gradient Norm: 0.0073, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:38:58] (step=0180480/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0017, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:41:52] (step=0180500/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0058, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 18:44:46] (step=0180520/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0013, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 18:47:40] (step=0180540/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 18:52:05] (step=0180560/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0011, Train Steps/Sec: 0.08, lr: 0.000100
[2024-10-27 18:54:59] (step=0180580/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 18:58:54] (step=0180600/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0025, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-27 19:01:47] (step=0180620/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0078, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 19:04:41] (step=0180640/epoch=0011) Train Loss: 0.0065, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:07:34] (step=0180660/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0065, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 19:10:29] (step=0180680/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0020, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:13:24] (step=0180700/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0053, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:16:20] (step=0180720/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:19:14] (step=0180740/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0047, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:22:08] (step=0180760/epoch=0011) Train Loss: 0.0066, Gradient Norm: 0.0010, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 19:25:14] (step=0180780/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0066, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:28:08] (step=0180800/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0006, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 19:31:02] (step=0180820/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0055, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:33:56] (step=0180840/epoch=0011) Train Loss: 0.0099, Gradient Norm: 0.0018, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:36:51] (step=0180860/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:39:46] (step=0180880/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:42:40] (step=0180900/epoch=0011) Train Loss: 0.0088, Gradient Norm: 0.0050, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:45:34] (step=0180920/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0015, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:48:27] (step=0180940/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0070, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 19:51:21] (step=0180960/epoch=0011) Train Loss: 0.0081, Gradient Norm: 0.0023, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 19:54:14] (step=0180980/epoch=0011) Train Loss: 0.0111, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 19:57:09] (step=0181000/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0026, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:00:02] (step=0181020/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0055, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:02:56] (step=0181040/epoch=0011) Train Loss: 0.0092, Gradient Norm: 0.0010, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:05:51] (step=0181060/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:08:48] (step=0181080/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0013, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:11:43] (step=0181100/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:14:36] (step=0181120/epoch=0011) Train Loss: 0.0076, Gradient Norm: 0.0013, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:17:31] (step=0181140/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:20:25] (step=0181160/epoch=0011) Train Loss: 0.0099, Gradient Norm: 0.0018, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:23:19] (step=0181180/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0074, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:26:12] (step=0181200/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0024, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:29:07] (step=0181220/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:32:01] (step=0181240/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:34:54] (step=0181260/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0049, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:37:48] (step=0181280/epoch=0011) Train Loss: 0.0102, Gradient Norm: 0.0023, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:40:41] (step=0181300/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0068, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:43:35] (step=0181320/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0027, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:46:29] (step=0181340/epoch=0011) Train Loss: 0.0080, Gradient Norm: 0.0056, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:49:28] (step=0181360/epoch=0011) Train Loss: 0.0103, Gradient Norm: 0.0024, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:52:22] (step=0181380/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0062, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 20:55:27] (step=0181400/epoch=0011) Train Loss: 0.0085, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 20:58:21] (step=0181420/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0093, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:01:14] (step=0181440/epoch=0011) Train Loss: 0.0077, Gradient Norm: 0.0022, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:04:08] (step=0181460/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:07:02] (step=0181480/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:09:57] (step=0181500/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0076, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:13:30] (step=0181520/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0019, Train Steps/Sec: 0.09, lr: 0.000100
[2024-10-27 21:16:25] (step=0181540/epoch=0011) Train Loss: 0.0104, Gradient Norm: 0.0103, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:19:18] (step=0181560/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0016, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:22:13] (step=0181580/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0086, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:25:07] (step=0181600/epoch=0011) Train Loss: 0.0078, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:28:01] (step=0181620/epoch=0011) Train Loss: 0.0075, Gradient Norm: 0.0083, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:30:56] (step=0181640/epoch=0011) Train Loss: 0.0070, Gradient Norm: 0.0019, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:33:49] (step=0181660/epoch=0011) Train Loss: 0.0066, Gradient Norm: 0.0061, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:36:43] (step=0181680/epoch=0011) Train Loss: 0.0094, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:39:37] (step=0181700/epoch=0011) Train Loss: 0.0111, Gradient Norm: 0.0080, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:42:57] (step=0181720/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0010, Train Steps/Sec: 0.10, lr: 0.000100
[2024-10-27 21:45:50] (step=0181740/epoch=0011) Train Loss: 0.0082, Gradient Norm: 0.0058, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:48:44] (step=0181760/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0014, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:51:40] (step=0181780/epoch=0011) Train Loss: 0.0086, Gradient Norm: 0.0056, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 21:54:34] (step=0181800/epoch=0011) Train Loss: 0.0090, Gradient Norm: 0.0018, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 21:57:29] (step=0181820/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0060, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:00:22] (step=0181840/epoch=0011) Train Loss: 0.0087, Gradient Norm: 0.0026, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 22:03:17] (step=0181860/epoch=0011) Train Loss: 0.0079, Gradient Norm: 0.0085, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:06:11] (step=0181880/epoch=0011) Train Loss: 0.0095, Gradient Norm: 0.0022, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:09:06] (step=0181900/epoch=0011) Train Loss: 0.0097, Gradient Norm: 0.0079, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:11:59] (step=0181920/epoch=0011) Train Loss: 0.0091, Gradient Norm: 0.0021, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 22:14:53] (step=0181940/epoch=0011) Train Loss: 0.0083, Gradient Norm: 0.0062, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:17:47] (step=0181960/epoch=0011) Train Loss: 0.0084, Gradient Norm: 0.0017, Train Steps/Sec: 0.12, lr: 0.000100
[2024-10-27 22:20:42] (step=0181980/epoch=0011) Train Loss: 0.0089, Gradient Norm: 0.0059, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:23:36] (step=0182000/epoch=0011) Train Loss: 0.0093, Gradient Norm: 0.0021, Train Steps/Sec: 0.11, lr: 0.000100
[2024-10-27 22:24:12] Error when saving at ./large-video-v2/results_long_all_64/053-UNet-vimeoshot-Gc-320_512/checkpoints/0182000.pt
