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[2023-10-25 04:47:08,346::train::INFO] [train] Iter 591761 | loss 0.9975 | loss(rot) 0.6578 | loss(pos) 0.0899 | loss(seq) 0.2498 | grad 4.1630 | lr 0.0000 | time_forward 1.0510 | time_backward 1.1770 |
[2023-10-25 04:47:13,870::train::INFO] [train] Iter 591762 | loss 1.2257 | loss(rot) 0.5175 | loss(pos) 0.3611 | loss(seq) 0.3471 | grad 7.5310 | lr 0.0000 | time_forward 2.4330 | time_backward 3.0880 |
[2023-10-25 04:47:21,829::train::INFO] [train] Iter 591763 | loss 0.2559 | loss(rot) 0.1150 | loss(pos) 0.0453 | loss(seq) 0.0956 | grad 2.2424 | lr 0.0000 | time_forward 3.4720 | time_backward 4.4830 |
[2023-10-25 04:47:30,736::train::INFO] [train] Iter 591764 | loss 0.6562 | loss(rot) 0.0813 | loss(pos) 0.5587 | loss(seq) 0.0162 | grad 3.0865 | lr 0.0000 | time_forward 3.6380 | time_backward 5.2660 |
[2023-10-25 04:47:39,659::train::INFO] [train] Iter 591765 | loss 0.2941 | loss(rot) 0.0608 | loss(pos) 0.0549 | loss(seq) 0.1783 | grad 2.1692 | lr 0.0000 | time_forward 3.6330 | time_backward 5.2860 |
[2023-10-25 04:47:47,523::train::INFO] [train] Iter 591766 | loss 0.2620 | loss(rot) 0.2158 | loss(pos) 0.0451 | loss(seq) 0.0011 | grad 2.5988 | lr 0.0000 | time_forward 3.3380 | time_backward 4.5230 |
[2023-10-25 04:47:54,997::train::INFO] [train] Iter 591767 | loss 0.4595 | loss(rot) 0.1544 | loss(pos) 0.0713 | loss(seq) 0.2338 | grad 3.5226 | lr 0.0000 | time_forward 3.1780 | time_backward 4.2920 |
[2023-10-25 04:48:02,704::train::INFO] [train] Iter 591768 | loss 2.2184 | loss(rot) 2.0102 | loss(pos) 0.0792 | loss(seq) 0.1290 | grad 5.4100 | lr 0.0000 | time_forward 3.2910 | time_backward 4.4130 |
[2023-10-25 04:48:05,553::train::INFO] [train] Iter 591769 | loss 1.1527 | loss(rot) 0.3762 | loss(pos) 0.4480 | loss(seq) 0.3284 | grad 5.9890 | lr 0.0000 | time_forward 1.3340 | time_backward 1.5120 |
[2023-10-25 04:48:13,249::train::INFO] [train] Iter 591770 | loss 0.2003 | loss(rot) 0.0898 | loss(pos) 0.0738 | loss(seq) 0.0366 | grad 2.4120 | lr 0.0000 | time_forward 3.2800 | time_backward 4.4140 |
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