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[2023-10-22 15:06:21,408::train::INFO] [train] Iter 558894 | loss 1.3397 | loss(rot) 0.4413 | loss(pos) 0.5131 | loss(seq) 0.3853 | grad 5.4870 | lr 0.0000 | time_forward 1.4070 | time_backward 1.4990 |
[2023-10-22 15:06:24,495::train::INFO] [train] Iter 558895 | loss 0.4110 | loss(rot) 0.1318 | loss(pos) 0.0396 | loss(seq) 0.2396 | grad 3.0608 | lr 0.0000 | time_forward 1.4370 | time_backward 1.6170 |
[2023-10-22 15:06:32,994::train::INFO] [train] Iter 558896 | loss 0.4419 | loss(rot) 0.1342 | loss(pos) 0.0390 | loss(seq) 0.2687 | grad 3.1711 | lr 0.0000 | time_forward 3.5400 | time_backward 4.9540 |
[2023-10-22 15:06:42,999::train::INFO] [train] Iter 558897 | loss 0.5796 | loss(rot) 0.0207 | loss(pos) 0.5562 | loss(seq) 0.0027 | grad 7.2613 | lr 0.0000 | time_forward 4.1180 | time_backward 5.8830 |
[2023-10-22 15:06:45,668::train::INFO] [train] Iter 558898 | loss 2.5018 | loss(rot) 2.4662 | loss(pos) 0.0355 | loss(seq) 0.0001 | grad 6.3341 | lr 0.0000 | time_forward 1.2820 | time_backward 1.3850 |
[2023-10-22 15:07:00,148::train::INFO] [train] Iter 558899 | loss 0.7807 | loss(rot) 0.1172 | loss(pos) 0.0617 | loss(seq) 0.6018 | grad 3.0388 | lr 0.0000 | time_forward 3.7440 | time_backward 5.1830 |
[2023-10-22 15:07:09,932::train::INFO] [train] Iter 558900 | loss 1.3636 | loss(rot) 0.0164 | loss(pos) 1.3469 | loss(seq) 0.0004 | grad 17.8302 | lr 0.0000 | time_forward 3.8810 | time_backward 5.8990 |
[2023-10-22 15:07:20,291::train::INFO] [train] Iter 558901 | loss 1.9869 | loss(rot) 1.8755 | loss(pos) 0.0765 | loss(seq) 0.0349 | grad 3.5054 | lr 0.0000 | time_forward 4.3600 | time_backward 5.9970 |
[2023-10-22 15:07:28,023::train::INFO] [train] Iter 558902 | loss 1.7536 | loss(rot) 1.4236 | loss(pos) 0.1199 | loss(seq) 0.2101 | grad 18.6969 | lr 0.0000 | time_forward 3.1620 | time_backward 4.5650 |
[2023-10-22 15:07:38,115::train::INFO] [train] Iter 558903 | loss 0.1528 | loss(rot) 0.0941 | loss(pos) 0.0105 | loss(seq) 0.0483 | grad 1.7503 | lr 0.0000 | time_forward 4.2490 | time_backward 5.8390 |
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