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[2023-10-23 19:40:16,963::train::INFO] [train] Iter 574678 | loss 1.3282 | loss(rot) 0.7908 | loss(pos) 0.3526 | loss(seq) 0.1849 | grad 6.4587 | lr 0.0000 | time_forward 2.8980 | time_backward 4.0160 |
[2023-10-23 19:40:26,383::train::INFO] [train] Iter 574679 | loss 0.9157 | loss(rot) 0.7040 | loss(pos) 0.0238 | loss(seq) 0.1879 | grad 3.7878 | lr 0.0000 | time_forward 3.8850 | time_backward 5.4580 |
[2023-10-23 19:40:29,160::train::INFO] [train] Iter 574680 | loss 0.9400 | loss(rot) 0.1211 | loss(pos) 0.8107 | loss(seq) 0.0081 | grad 7.5478 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4590 |
[2023-10-23 19:40:38,666::train::INFO] [train] Iter 574681 | loss 1.0055 | loss(rot) 0.7922 | loss(pos) 0.0267 | loss(seq) 0.1865 | grad 12.8124 | lr 0.0000 | time_forward 3.9160 | time_backward 5.5510 |
[2023-10-23 19:40:48,088::train::INFO] [train] Iter 574682 | loss 1.1780 | loss(rot) 0.8604 | loss(pos) 0.0767 | loss(seq) 0.2410 | grad 2.5716 | lr 0.0000 | time_forward 3.9940 | time_backward 5.4240 |
[2023-10-23 19:40:50,372::train::INFO] [train] Iter 574683 | loss 0.9683 | loss(rot) 0.1926 | loss(pos) 0.7402 | loss(seq) 0.0356 | grad 5.9994 | lr 0.0000 | time_forward 1.0390 | time_backward 1.2420 |
[2023-10-23 19:41:01,424::train::INFO] [train] Iter 574684 | loss 1.9319 | loss(rot) 1.5250 | loss(pos) 0.1181 | loss(seq) 0.2888 | grad 6.1809 | lr 0.0000 | time_forward 5.7650 | time_backward 5.2850 |
[2023-10-23 19:41:12,940::train::INFO] [train] Iter 574685 | loss 0.6862 | loss(rot) 0.6382 | loss(pos) 0.0278 | loss(seq) 0.0202 | grad 4.5877 | lr 0.0000 | time_forward 5.7650 | time_backward 5.7480 |
[2023-10-23 19:41:17,756::train::INFO] [train] Iter 574686 | loss 1.0207 | loss(rot) 0.5146 | loss(pos) 0.0903 | loss(seq) 0.4158 | grad 3.0301 | lr 0.0000 | time_forward 2.1620 | time_backward 2.6500 |
[2023-10-23 19:41:25,959::train::INFO] [train] Iter 574687 | loss 0.2259 | loss(rot) 0.0843 | loss(pos) 0.1155 | loss(seq) 0.0261 | grad 2.7962 | lr 0.0000 | time_forward 3.4260 | time_backward 4.7740 |
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