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[2023-10-23 06:52:08,856::train::INFO] [train] Iter 567685 | loss 0.3166 | loss(rot) 0.0857 | loss(pos) 0.0597 | loss(seq) 0.1712 | grad 2.5604 | lr 0.0000 | time_forward 1.2710 | time_backward 1.3960 |
[2023-10-23 06:52:11,631::train::INFO] [train] Iter 567686 | loss 1.4023 | loss(rot) 0.8646 | loss(pos) 0.1255 | loss(seq) 0.4122 | grad 4.5790 | lr 0.0000 | time_forward 1.2480 | time_backward 1.5170 |
[2023-10-23 06:52:14,313::train::INFO] [train] Iter 567687 | loss 1.3680 | loss(rot) 1.1479 | loss(pos) 0.1008 | loss(seq) 0.1193 | grad 5.8409 | lr 0.0000 | time_forward 1.2750 | time_backward 1.4020 |
[2023-10-23 06:52:20,779::train::INFO] [train] Iter 567688 | loss 1.4907 | loss(rot) 0.4943 | loss(pos) 0.9883 | loss(seq) 0.0081 | grad 8.3478 | lr 0.0000 | time_forward 2.7650 | time_backward 3.6840 |
[2023-10-23 06:52:23,455::train::INFO] [train] Iter 567689 | loss 0.6617 | loss(rot) 0.2053 | loss(pos) 0.0523 | loss(seq) 0.4041 | grad 3.0244 | lr 0.0000 | time_forward 1.2490 | time_backward 1.4230 |
[2023-10-23 06:52:30,593::train::INFO] [train] Iter 567690 | loss 0.5759 | loss(rot) 0.4993 | loss(pos) 0.0201 | loss(seq) 0.0566 | grad 3.5508 | lr 0.0000 | time_forward 3.0750 | time_backward 4.0590 |
[2023-10-23 06:52:33,229::train::INFO] [train] Iter 567691 | loss 0.7088 | loss(rot) 0.4631 | loss(pos) 0.0401 | loss(seq) 0.2055 | grad 13.6857 | lr 0.0000 | time_forward 1.2550 | time_backward 1.3790 |
[2023-10-23 06:52:35,809::train::INFO] [train] Iter 567692 | loss 1.1795 | loss(rot) 0.6851 | loss(pos) 0.1078 | loss(seq) 0.3866 | grad 3.4678 | lr 0.0000 | time_forward 1.1800 | time_backward 1.3820 |
[2023-10-23 06:52:42,475::train::INFO] [train] Iter 567693 | loss 0.9582 | loss(rot) 0.1999 | loss(pos) 0.0526 | loss(seq) 0.7057 | grad 4.2919 | lr 0.0000 | time_forward 2.8340 | time_backward 3.8140 |
[2023-10-23 06:52:49,181::train::INFO] [train] Iter 567694 | loss 1.5757 | loss(rot) 0.9157 | loss(pos) 0.0930 | loss(seq) 0.5670 | grad 4.0224 | lr 0.0000 | time_forward 2.8260 | time_backward 3.8780 |
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