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[2023-10-23 06:42:13,215::train::INFO] [train] Iter 567584 | loss 0.3400 | loss(rot) 0.0648 | loss(pos) 0.0588 | loss(seq) 0.2164 | grad 2.5191 | lr 0.0000 | time_forward 3.3800 | time_backward 4.2280 |
[2023-10-23 06:42:20,298::train::INFO] [train] Iter 567585 | loss 1.1338 | loss(rot) 0.4676 | loss(pos) 0.1876 | loss(seq) 0.4786 | grad 4.2596 | lr 0.0000 | time_forward 3.1990 | time_backward 3.8800 |
[2023-10-23 06:42:22,934::train::INFO] [train] Iter 567586 | loss 0.8307 | loss(rot) 0.4230 | loss(pos) 0.0256 | loss(seq) 0.3822 | grad 6.2281 | lr 0.0000 | time_forward 1.2670 | time_backward 1.3650 |
[2023-10-23 06:42:29,816::train::INFO] [train] Iter 567587 | loss 0.5611 | loss(rot) 0.1873 | loss(pos) 0.0676 | loss(seq) 0.3061 | grad 3.8779 | lr 0.0000 | time_forward 3.0450 | time_backward 3.8340 |
[2023-10-23 06:42:38,196::train::INFO] [train] Iter 567588 | loss 0.4442 | loss(rot) 0.1117 | loss(pos) 0.2497 | loss(seq) 0.0828 | grad 5.0767 | lr 0.0000 | time_forward 3.6110 | time_backward 4.7660 |
[2023-10-23 06:42:40,926::train::INFO] [train] Iter 567589 | loss 0.3480 | loss(rot) 0.0894 | loss(pos) 0.2526 | loss(seq) 0.0060 | grad 4.8416 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4510 |
[2023-10-23 06:42:48,962::train::INFO] [train] Iter 567590 | loss 0.8912 | loss(rot) 0.5607 | loss(pos) 0.0471 | loss(seq) 0.2834 | grad 3.9903 | lr 0.0000 | time_forward 3.3340 | time_backward 4.6780 |
[2023-10-23 06:42:55,481::train::INFO] [train] Iter 567591 | loss 0.5484 | loss(rot) 0.2259 | loss(pos) 0.0300 | loss(seq) 0.2926 | grad 3.1974 | lr 0.0000 | time_forward 2.8310 | time_backward 3.6850 |
[2023-10-23 06:43:03,820::train::INFO] [train] Iter 567592 | loss 0.6367 | loss(rot) 0.3105 | loss(pos) 0.0606 | loss(seq) 0.2656 | grad 3.4390 | lr 0.0000 | time_forward 3.6430 | time_backward 4.6930 |
[2023-10-23 06:43:06,447::train::INFO] [train] Iter 567593 | loss 1.6086 | loss(rot) 1.3947 | loss(pos) 0.0354 | loss(seq) 0.1784 | grad 3.8799 | lr 0.0000 | time_forward 1.2390 | time_backward 1.3850 |
[2023-10-23 06:43:14,073::train::INFO] [train] Iter 567594 | loss 0.6045 | loss(rot) 0.4745 | loss(pos) 0.0272 | loss(seq) 0.1028 | grad 2.4017 | lr 0.0000 | time_forward 3.3710 | time_backward 4.2510 |
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