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[2023-10-25 13:10:31,866::train::INFO] [train] Iter 596156 | loss 0.5032 | loss(rot) 0.1751 | loss(pos) 0.1385 | loss(seq) 0.1896 | grad 2.6169 | lr 0.0000 | time_forward 3.9890 | time_backward 6.0030 |
[2023-10-25 13:10:41,739::train::INFO] [train] Iter 596157 | loss 0.7592 | loss(rot) 0.0679 | loss(pos) 0.2043 | loss(seq) 0.4870 | grad 5.6388 | lr 0.0000 | time_forward 4.0760 | time_backward 5.7940 |
[2023-10-25 13:10:49,912::train::INFO] [train] Iter 596158 | loss 0.3043 | loss(rot) 0.0876 | loss(pos) 0.2042 | loss(seq) 0.0125 | grad 4.0517 | lr 0.0000 | time_forward 3.4490 | time_backward 4.7200 |
[2023-10-25 13:10:52,773::train::INFO] [train] Iter 596159 | loss 0.5834 | loss(rot) 0.5226 | loss(pos) 0.0231 | loss(seq) 0.0377 | grad 3.2941 | lr 0.0000 | time_forward 1.3650 | time_backward 1.4930 |
[2023-10-25 13:11:00,945::train::INFO] [train] Iter 596160 | loss 0.2790 | loss(rot) 0.1248 | loss(pos) 0.1389 | loss(seq) 0.0153 | grad 3.3020 | lr 0.0000 | time_forward 3.4490 | time_backward 4.7190 |
[2023-10-25 13:11:09,259::train::INFO] [train] Iter 596161 | loss 1.7389 | loss(rot) 1.3518 | loss(pos) 0.0836 | loss(seq) 0.3035 | grad 6.1593 | lr 0.0000 | time_forward 3.4590 | time_backward 4.8510 |
[2023-10-25 13:11:19,314::train::INFO] [train] Iter 596162 | loss 0.5321 | loss(rot) 0.2320 | loss(pos) 0.2532 | loss(seq) 0.0469 | grad 3.7557 | lr 0.0000 | time_forward 4.2750 | time_backward 5.7770 |
[2023-10-25 13:11:29,311::train::INFO] [train] Iter 596163 | loss 2.3231 | loss(rot) 2.2395 | loss(pos) 0.0738 | loss(seq) 0.0097 | grad 5.3023 | lr 0.0000 | time_forward 4.1400 | time_backward 5.8540 |
[2023-10-25 13:11:32,117::train::INFO] [train] Iter 596164 | loss 1.5932 | loss(rot) 1.2055 | loss(pos) 0.1869 | loss(seq) 0.2007 | grad 15.8779 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4940 |
[2023-10-25 13:11:39,792::train::INFO] [train] Iter 596165 | loss 2.2816 | loss(rot) 1.2404 | loss(pos) 0.5514 | loss(seq) 0.4897 | grad 4.3166 | lr 0.0000 | time_forward 3.3150 | time_backward 4.3550 |
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