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[2023-09-03 00:07:29,721::train::INFO] [train] Iter 15670 | loss 1.2838 | loss(rot) 1.1830 | loss(pos) 0.0978 | loss(seq) 0.0030 | grad 4.1037 | lr 0.0010 | time_forward 4.7110 | time_backward 7.1520 |
[2023-09-03 00:07:41,691::train::INFO] [train] Iter 15671 | loss 0.9786 | loss(rot) 0.1002 | loss(pos) 0.8538 | loss(seq) 0.0246 | grad 6.2638 | lr 0.0010 | time_forward 4.7960 | time_backward 7.1700 |
[2023-09-03 00:07:51,982::train::INFO] [train] Iter 15672 | loss 1.4165 | loss(rot) 0.4373 | loss(pos) 0.3207 | loss(seq) 0.6585 | grad 5.0311 | lr 0.0010 | time_forward 4.3090 | time_backward 5.9780 |
[2023-09-03 00:08:01,456::train::INFO] [train] Iter 15673 | loss 1.1055 | loss(rot) 0.2120 | loss(pos) 0.3065 | loss(seq) 0.5870 | grad 4.2387 | lr 0.0010 | time_forward 3.9610 | time_backward 5.5100 |
[2023-09-03 00:08:12,076::train::INFO] [train] Iter 15674 | loss 2.1201 | loss(rot) 1.3214 | loss(pos) 0.4579 | loss(seq) 0.3408 | grad 5.1390 | lr 0.0010 | time_forward 4.3070 | time_backward 6.3100 |
[2023-09-03 00:08:14,994::train::INFO] [train] Iter 15675 | loss 1.3535 | loss(rot) 0.0482 | loss(pos) 1.3021 | loss(seq) 0.0032 | grad 5.9892 | lr 0.0010 | time_forward 1.3480 | time_backward 1.5660 |
[2023-09-03 00:08:26,546::train::INFO] [train] Iter 15676 | loss 2.1957 | loss(rot) 2.0107 | loss(pos) 0.1802 | loss(seq) 0.0047 | grad 7.9482 | lr 0.0010 | time_forward 5.4160 | time_backward 6.1330 |
[2023-09-03 00:08:29,476::train::INFO] [train] Iter 15677 | loss 1.0925 | loss(rot) 0.1260 | loss(pos) 0.9000 | loss(seq) 0.0665 | grad 8.7322 | lr 0.0010 | time_forward 1.4160 | time_backward 1.5080 |
[2023-09-03 00:08:40,185::train::INFO] [train] Iter 15678 | loss 0.8828 | loss(rot) 0.1111 | loss(pos) 0.7583 | loss(seq) 0.0134 | grad 5.1562 | lr 0.0010 | time_forward 4.0570 | time_backward 6.6480 |
[2023-09-03 00:08:48,048::train::INFO] [train] Iter 15679 | loss 1.1195 | loss(rot) 0.2880 | loss(pos) 0.3452 | loss(seq) 0.4863 | grad 5.4450 | lr 0.0010 | time_forward 3.0470 | time_backward 4.7940 |
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