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[2023-10-23 08:42:43,374::train::INFO] [train] Iter 568784 | loss 0.6396 | loss(rot) 0.2706 | loss(pos) 0.0810 | loss(seq) 0.2880 | grad 3.2005 | lr 0.0000 | time_forward 3.4590 | time_backward 4.5110 |
[2023-10-23 08:42:45,971::train::INFO] [train] Iter 568785 | loss 0.8051 | loss(rot) 0.4295 | loss(pos) 0.1707 | loss(seq) 0.2048 | grad 6.9860 | lr 0.0000 | time_forward 1.2350 | time_backward 1.3590 |
[2023-10-23 08:42:53,060::train::INFO] [train] Iter 568786 | loss 0.2794 | loss(rot) 0.0664 | loss(pos) 0.0896 | loss(seq) 0.1234 | grad 3.9019 | lr 0.0000 | time_forward 3.1270 | time_backward 3.9580 |
[2023-10-23 08:42:55,645::train::INFO] [train] Iter 568787 | loss 0.5327 | loss(rot) 0.0700 | loss(pos) 0.0686 | loss(seq) 0.3941 | grad 3.4422 | lr 0.0000 | time_forward 1.2010 | time_backward 1.3820 |
[2023-10-23 08:43:03,222::train::INFO] [train] Iter 568788 | loss 0.3102 | loss(rot) 0.0615 | loss(pos) 0.0277 | loss(seq) 0.2210 | grad 2.0342 | lr 0.0000 | time_forward 3.3420 | time_backward 4.2320 |
[2023-10-23 08:43:05,404::train::INFO] [train] Iter 568789 | loss 0.2505 | loss(rot) 0.2100 | loss(pos) 0.0202 | loss(seq) 0.0203 | grad 3.2126 | lr 0.0000 | time_forward 1.0070 | time_backward 1.1720 |
[2023-10-23 08:43:13,825::train::INFO] [train] Iter 568790 | loss 0.4820 | loss(rot) 0.2176 | loss(pos) 0.0672 | loss(seq) 0.1972 | grad 2.7534 | lr 0.0000 | time_forward 3.4020 | time_backward 5.0050 |
[2023-10-23 08:43:16,040::train::INFO] [train] Iter 568791 | loss 0.5347 | loss(rot) 0.1796 | loss(pos) 0.0543 | loss(seq) 0.3008 | grad 2.8706 | lr 0.0000 | time_forward 1.0230 | time_backward 1.1890 |
[2023-10-23 08:43:24,233::train::INFO] [train] Iter 568792 | loss 0.6147 | loss(rot) 0.5593 | loss(pos) 0.0360 | loss(seq) 0.0194 | grad 28.7163 | lr 0.0000 | time_forward 3.4050 | time_backward 4.7740 |
[2023-10-23 08:43:26,894::train::INFO] [train] Iter 568793 | loss 0.7569 | loss(rot) 0.2063 | loss(pos) 0.2412 | loss(seq) 0.3093 | grad 3.4730 | lr 0.0000 | time_forward 1.2620 | time_backward 1.3960 |
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