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[2023-10-25 11:46:50,901::train::INFO] [train] Iter 595458 | loss 0.1928 | loss(rot) 0.0942 | loss(pos) 0.0137 | loss(seq) 0.0849 | grad 1.8465 | lr 0.0000 | time_forward 3.8260 | time_backward 5.3230 |
[2023-10-25 11:46:59,703::train::INFO] [train] Iter 595459 | loss 1.4034 | loss(rot) 0.8369 | loss(pos) 0.2072 | loss(seq) 0.3593 | grad 3.3234 | lr 0.0000 | time_forward 3.6730 | time_backward 5.1260 |
[2023-10-25 11:47:06,739::train::INFO] [train] Iter 595460 | loss 0.5710 | loss(rot) 0.0674 | loss(pos) 0.0418 | loss(seq) 0.4618 | grad 2.8845 | lr 0.0000 | time_forward 3.0300 | time_backward 4.0030 |
[2023-10-25 11:47:10,036::train::INFO] [train] Iter 595461 | loss 1.3754 | loss(rot) 0.0044 | loss(pos) 1.3707 | loss(seq) 0.0003 | grad 12.3279 | lr 0.0000 | time_forward 1.4610 | time_backward 1.8320 |
[2023-10-25 11:47:20,029::train::INFO] [train] Iter 595462 | loss 0.3454 | loss(rot) 0.3094 | loss(pos) 0.0351 | loss(seq) 0.0009 | grad 68.2057 | lr 0.0000 | time_forward 4.0340 | time_backward 5.9430 |
[2023-10-25 11:47:22,854::train::INFO] [train] Iter 595463 | loss 0.4328 | loss(rot) 0.3749 | loss(pos) 0.0391 | loss(seq) 0.0188 | grad 2.4494 | lr 0.0000 | time_forward 1.3710 | time_backward 1.4510 |
[2023-10-25 11:47:30,429::train::INFO] [train] Iter 595464 | loss 0.3143 | loss(rot) 0.2821 | loss(pos) 0.0273 | loss(seq) 0.0049 | grad 10.5939 | lr 0.0000 | time_forward 3.2550 | time_backward 4.3180 |
[2023-10-25 11:47:40,547::train::INFO] [train] Iter 595465 | loss 0.9205 | loss(rot) 0.7140 | loss(pos) 0.0634 | loss(seq) 0.1431 | grad 14.4101 | lr 0.0000 | time_forward 4.2160 | time_backward 5.8980 |
[2023-10-25 11:47:43,233::train::INFO] [train] Iter 595466 | loss 0.5303 | loss(rot) 0.1175 | loss(pos) 0.0639 | loss(seq) 0.3489 | grad 3.3432 | lr 0.0000 | time_forward 1.2770 | time_backward 1.4060 |
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