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[2023-10-22 14:53:55,459::train::INFO] [train] Iter 558794 | loss 2.2348 | loss(rot) 1.8444 | loss(pos) 0.1112 | loss(seq) 0.2792 | grad 5.1402 | lr 0.0000 | time_forward 4.0520 | time_backward 5.9900 |
[2023-10-22 14:54:05,144::train::INFO] [train] Iter 558795 | loss 0.3970 | loss(rot) 0.0228 | loss(pos) 0.3700 | loss(seq) 0.0041 | grad 7.8054 | lr 0.0000 | time_forward 3.9150 | time_backward 5.7660 |
[2023-10-22 14:54:08,143::train::INFO] [train] Iter 558796 | loss 0.1757 | loss(rot) 0.0658 | loss(pos) 0.0561 | loss(seq) 0.0538 | grad 2.4311 | lr 0.0000 | time_forward 1.4060 | time_backward 1.5900 |
[2023-10-22 14:54:18,782::train::INFO] [train] Iter 558797 | loss 0.2374 | loss(rot) 0.1390 | loss(pos) 0.0926 | loss(seq) 0.0059 | grad 2.6913 | lr 0.0000 | time_forward 4.4630 | time_backward 6.1730 |
[2023-10-22 14:54:28,990::train::INFO] [train] Iter 558798 | loss 0.3638 | loss(rot) 0.1514 | loss(pos) 0.1485 | loss(seq) 0.0639 | grad 3.0539 | lr 0.0000 | time_forward 4.1330 | time_backward 6.0730 |
[2023-10-22 14:54:38,807::train::INFO] [train] Iter 558799 | loss 0.3461 | loss(rot) 0.1469 | loss(pos) 0.1957 | loss(seq) 0.0035 | grad 3.8206 | lr 0.0000 | time_forward 4.1420 | time_backward 5.6710 |
[2023-10-22 14:54:48,161::train::INFO] [train] Iter 558800 | loss 0.8903 | loss(rot) 0.8576 | loss(pos) 0.0183 | loss(seq) 0.0144 | grad 33.9245 | lr 0.0000 | time_forward 3.9440 | time_backward 5.4080 |
[2023-10-22 14:54:58,626::train::INFO] [train] Iter 558801 | loss 1.6390 | loss(rot) 1.0919 | loss(pos) 0.1350 | loss(seq) 0.4120 | grad 3.9932 | lr 0.0000 | time_forward 4.8400 | time_backward 5.6210 |
[2023-10-22 14:55:01,551::train::INFO] [train] Iter 558802 | loss 0.9335 | loss(rot) 0.4218 | loss(pos) 0.1556 | loss(seq) 0.3562 | grad 2.9220 | lr 0.0000 | time_forward 1.3650 | time_backward 1.5560 |
[2023-10-22 14:55:10,632::train::INFO] [train] Iter 558803 | loss 1.0912 | loss(rot) 0.7827 | loss(pos) 0.1075 | loss(seq) 0.2010 | grad 3.1810 | lr 0.0000 | time_forward 3.9030 | time_backward 5.1760 |
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