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[2023-10-23 05:42:01,425::train::INFO] [train] Iter 566986 | loss 0.3851 | loss(rot) 0.3140 | loss(pos) 0.0266 | loss(seq) 0.0445 | grad 4.2139 | lr 0.0000 | time_forward 1.2440 | time_backward 1.4610 |
[2023-10-23 05:42:04,119::train::INFO] [train] Iter 566987 | loss 0.6004 | loss(rot) 0.5535 | loss(pos) 0.0097 | loss(seq) 0.0372 | grad 2.6127 | lr 0.0000 | time_forward 1.2410 | time_backward 1.4490 |
[2023-10-23 05:42:10,070::train::INFO] [train] Iter 566988 | loss 1.2108 | loss(rot) 0.9675 | loss(pos) 0.0414 | loss(seq) 0.2019 | grad 5.5317 | lr 0.0000 | time_forward 2.6130 | time_backward 3.3350 |
[2023-10-23 05:42:16,374::train::INFO] [train] Iter 566989 | loss 0.6535 | loss(rot) 0.2216 | loss(pos) 0.3459 | loss(seq) 0.0861 | grad 4.0241 | lr 0.0000 | time_forward 2.6980 | time_backward 3.6030 |
[2023-10-23 05:42:24,341::train::INFO] [train] Iter 566990 | loss 1.0326 | loss(rot) 0.4953 | loss(pos) 0.0465 | loss(seq) 0.4908 | grad 4.6839 | lr 0.0000 | time_forward 3.2770 | time_backward 4.6860 |
[2023-10-23 05:42:31,249::train::INFO] [train] Iter 566991 | loss 1.8229 | loss(rot) 1.6246 | loss(pos) 0.0400 | loss(seq) 0.1582 | grad 4.6645 | lr 0.0000 | time_forward 2.9650 | time_backward 3.9410 |
[2023-10-23 05:42:33,894::train::INFO] [train] Iter 566992 | loss 1.7497 | loss(rot) 0.7935 | loss(pos) 0.3122 | loss(seq) 0.6440 | grad 4.3902 | lr 0.0000 | time_forward 1.1980 | time_backward 1.4430 |
[2023-10-23 05:42:36,534::train::INFO] [train] Iter 566993 | loss 0.3883 | loss(rot) 0.0453 | loss(pos) 0.3333 | loss(seq) 0.0097 | grad 11.6091 | lr 0.0000 | time_forward 1.2340 | time_backward 1.3860 |
[2023-10-23 05:42:44,524::train::INFO] [train] Iter 566994 | loss 0.9460 | loss(rot) 0.7977 | loss(pos) 0.0331 | loss(seq) 0.1153 | grad 3.2745 | lr 0.0000 | time_forward 3.3180 | time_backward 4.6690 |
[2023-10-23 05:42:47,217::train::INFO] [train] Iter 566995 | loss 0.6295 | loss(rot) 0.1289 | loss(pos) 0.4500 | loss(seq) 0.0506 | grad 3.1812 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4170 |
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