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[2023-10-23 16:24:25,360::train::INFO] [train] Iter 572980 | loss 0.7091 | loss(rot) 0.6858 | loss(pos) 0.0213 | loss(seq) 0.0020 | grad 38.4062 | lr 0.0000 | time_forward 3.9860 | time_backward 5.4100 |
[2023-10-23 16:24:28,082::train::INFO] [train] Iter 572981 | loss 0.3567 | loss(rot) 0.0489 | loss(pos) 0.2996 | loss(seq) 0.0082 | grad 5.0468 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4220 |
[2023-10-23 16:24:37,698::train::INFO] [train] Iter 572982 | loss 0.3237 | loss(rot) 0.2776 | loss(pos) 0.0453 | loss(seq) 0.0008 | grad 15.7627 | lr 0.0000 | time_forward 4.2110 | time_backward 5.4020 |
[2023-10-23 16:24:47,342::train::INFO] [train] Iter 572983 | loss 0.6660 | loss(rot) 0.1690 | loss(pos) 0.2368 | loss(seq) 0.2603 | grad 3.7410 | lr 0.0000 | time_forward 4.1950 | time_backward 5.4460 |
[2023-10-23 16:24:56,576::train::INFO] [train] Iter 572984 | loss 0.4867 | loss(rot) 0.4755 | loss(pos) 0.0109 | loss(seq) 0.0003 | grad 8.5134 | lr 0.0000 | time_forward 3.7720 | time_backward 5.4580 |
[2023-10-23 16:25:04,786::train::INFO] [train] Iter 572985 | loss 0.3232 | loss(rot) 0.1412 | loss(pos) 0.0394 | loss(seq) 0.1426 | grad 3.0319 | lr 0.0000 | time_forward 3.6510 | time_backward 4.5560 |
[2023-10-23 16:25:12,727::train::INFO] [train] Iter 572986 | loss 1.4094 | loss(rot) 1.1706 | loss(pos) 0.0374 | loss(seq) 0.2014 | grad 4.2717 | lr 0.0000 | time_forward 3.4770 | time_backward 4.4610 |
[2023-10-23 16:25:22,083::train::INFO] [train] Iter 572987 | loss 1.4926 | loss(rot) 1.0191 | loss(pos) 0.0890 | loss(seq) 0.3845 | grad 4.6377 | lr 0.0000 | time_forward 3.7660 | time_backward 5.5870 |
[2023-10-23 16:25:24,829::train::INFO] [train] Iter 572988 | loss 0.9552 | loss(rot) 0.6121 | loss(pos) 0.0793 | loss(seq) 0.2638 | grad 3.4267 | lr 0.0000 | time_forward 1.3030 | time_backward 1.4410 |
[2023-10-23 16:25:33,707::train::INFO] [train] Iter 572989 | loss 0.8838 | loss(rot) 0.0453 | loss(pos) 0.8345 | loss(seq) 0.0041 | grad 7.5958 | lr 0.0000 | time_forward 3.8830 | time_backward 4.9670 |
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