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[2023-10-25 07:50:35,024::train::INFO] [train] Iter 593459 | loss 0.8146 | loss(rot) 0.7729 | loss(pos) 0.0412 | loss(seq) 0.0005 | grad 53.7190 | lr 0.0000 | time_forward 3.6120 | time_backward 4.7530 |
[2023-10-25 07:50:42,739::train::INFO] [train] Iter 593460 | loss 0.5475 | loss(rot) 0.2810 | loss(pos) 0.0622 | loss(seq) 0.2042 | grad 3.8756 | lr 0.0000 | time_forward 3.3010 | time_backward 4.4110 |
[2023-10-25 07:50:51,065::train::INFO] [train] Iter 593461 | loss 0.7976 | loss(rot) 0.4142 | loss(pos) 0.1693 | loss(seq) 0.2141 | grad 3.4812 | lr 0.0000 | time_forward 3.5770 | time_backward 4.7450 |
[2023-10-25 07:51:00,030::train::INFO] [train] Iter 593462 | loss 0.2345 | loss(rot) 0.0708 | loss(pos) 0.0592 | loss(seq) 0.1045 | grad 2.2312 | lr 0.0000 | time_forward 3.7230 | time_backward 5.2400 |
[2023-10-25 07:51:02,790::train::INFO] [train] Iter 593463 | loss 0.5567 | loss(rot) 0.0677 | loss(pos) 0.1985 | loss(seq) 0.2906 | grad 4.9369 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4550 |
[2023-10-25 07:51:09,960::train::INFO] [train] Iter 593464 | loss 1.5859 | loss(rot) 1.5482 | loss(pos) 0.0343 | loss(seq) 0.0034 | grad 5.7350 | lr 0.0000 | time_forward 3.0550 | time_backward 4.0970 |
[2023-10-25 07:51:17,218::train::INFO] [train] Iter 593465 | loss 0.3203 | loss(rot) 0.1584 | loss(pos) 0.0255 | loss(seq) 0.1363 | grad 2.5927 | lr 0.0000 | time_forward 3.0940 | time_backward 4.1620 |
[2023-10-25 07:51:24,416::train::INFO] [train] Iter 593466 | loss 0.2906 | loss(rot) 0.0353 | loss(pos) 0.0884 | loss(seq) 0.1669 | grad 2.8928 | lr 0.0000 | time_forward 3.0620 | time_backward 4.1320 |
[2023-10-25 07:51:29,854::train::INFO] [train] Iter 593467 | loss 1.3957 | loss(rot) 0.3074 | loss(pos) 0.6202 | loss(seq) 0.4680 | grad 6.4243 | lr 0.0000 | time_forward 2.3350 | time_backward 3.1000 |
[2023-10-25 07:51:38,036::train::INFO] [train] Iter 593468 | loss 0.1730 | loss(rot) 0.0941 | loss(pos) 0.0339 | loss(seq) 0.0451 | grad 2.2107 | lr 0.0000 | time_forward 3.4960 | time_backward 4.6840 |
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