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[2023-10-24 03:43:58,913::train::INFO] [train] Iter 578774 | loss 0.4786 | loss(rot) 0.1921 | loss(pos) 0.0650 | loss(seq) 0.2215 | grad 3.4436 | lr 0.0000 | time_forward 3.0300 | time_backward 4.1270 |
[2023-10-24 03:44:08,891::train::INFO] [train] Iter 578775 | loss 0.9300 | loss(rot) 0.3149 | loss(pos) 0.3716 | loss(seq) 0.2435 | grad 2.9030 | lr 0.0000 | time_forward 4.1460 | time_backward 5.8270 |
[2023-10-24 03:44:17,608::train::INFO] [train] Iter 578776 | loss 0.9217 | loss(rot) 0.1259 | loss(pos) 0.7920 | loss(seq) 0.0038 | grad 6.6344 | lr 0.0000 | time_forward 3.7210 | time_backward 4.9920 |
[2023-10-24 03:44:27,495::train::INFO] [train] Iter 578777 | loss 0.4284 | loss(rot) 0.2295 | loss(pos) 0.0341 | loss(seq) 0.1648 | grad 27.5158 | lr 0.0000 | time_forward 3.8820 | time_backward 6.0020 |
[2023-10-24 03:44:30,338::train::INFO] [train] Iter 578778 | loss 1.3858 | loss(rot) 1.2321 | loss(pos) 0.0491 | loss(seq) 0.1046 | grad 5.5609 | lr 0.0000 | time_forward 1.3610 | time_backward 1.4780 |
[2023-10-24 03:44:38,987::train::INFO] [train] Iter 578779 | loss 0.3674 | loss(rot) 0.2280 | loss(pos) 0.0665 | loss(seq) 0.0730 | grad 3.5672 | lr 0.0000 | time_forward 3.6330 | time_backward 4.9760 |
[2023-10-24 03:44:49,173::train::INFO] [train] Iter 578780 | loss 0.2219 | loss(rot) 0.1149 | loss(pos) 0.0633 | loss(seq) 0.0436 | grad 2.7440 | lr 0.0000 | time_forward 4.0580 | time_backward 6.1240 |
[2023-10-24 03:44:55,582::train::INFO] [train] Iter 578781 | loss 0.4458 | loss(rot) 0.3444 | loss(pos) 0.0176 | loss(seq) 0.0839 | grad 2.9371 | lr 0.0000 | time_forward 2.7370 | time_backward 3.6690 |
[2023-10-24 03:45:03,824::train::INFO] [train] Iter 578782 | loss 0.4228 | loss(rot) 0.1487 | loss(pos) 0.0771 | loss(seq) 0.1970 | grad 4.1633 | lr 0.0000 | time_forward 3.5020 | time_backward 4.7280 |
[2023-10-24 03:45:13,751::train::INFO] [train] Iter 578783 | loss 0.8780 | loss(rot) 0.6253 | loss(pos) 0.0733 | loss(seq) 0.1793 | grad 6.1015 | lr 0.0000 | time_forward 4.2080 | time_backward 5.7160 |
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