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[2023-10-22 21:07:07,810::train::INFO] [train] Iter 561991 | loss 0.5688 | loss(rot) 0.0585 | loss(pos) 0.4433 | loss(seq) 0.0670 | grad 7.1292 | lr 0.0000 | time_forward 1.2750 | time_backward 1.4460 |
[2023-10-22 21:07:15,901::train::INFO] [train] Iter 561992 | loss 1.2897 | loss(rot) 0.6753 | loss(pos) 0.2583 | loss(seq) 0.3561 | grad 4.0628 | lr 0.0000 | time_forward 3.4550 | time_backward 4.6130 |
[2023-10-22 21:07:18,615::train::INFO] [train] Iter 561993 | loss 0.5791 | loss(rot) 0.3552 | loss(pos) 0.0215 | loss(seq) 0.2024 | grad 2.5140 | lr 0.0000 | time_forward 1.2850 | time_backward 1.4250 |
[2023-10-22 21:07:27,464::train::INFO] [train] Iter 561994 | loss 0.9412 | loss(rot) 0.5242 | loss(pos) 0.0353 | loss(seq) 0.3817 | grad 4.2457 | lr 0.0000 | time_forward 3.5920 | time_backward 5.2540 |
[2023-10-22 21:07:36,235::train::INFO] [train] Iter 561995 | loss 1.6898 | loss(rot) 1.5630 | loss(pos) 0.0342 | loss(seq) 0.0926 | grad 4.1879 | lr 0.0000 | time_forward 3.5890 | time_backward 5.1790 |
[2023-10-22 21:07:43,490::train::INFO] [train] Iter 561996 | loss 0.5710 | loss(rot) 0.4818 | loss(pos) 0.0134 | loss(seq) 0.0758 | grad 3.6872 | lr 0.0000 | time_forward 3.0800 | time_backward 4.1730 |
[2023-10-22 21:07:50,624::train::INFO] [train] Iter 561997 | loss 0.6751 | loss(rot) 0.0235 | loss(pos) 0.4794 | loss(seq) 0.1721 | grad 7.3782 | lr 0.0000 | time_forward 2.9900 | time_backward 4.1410 |
[2023-10-22 21:07:59,403::train::INFO] [train] Iter 561998 | loss 0.6870 | loss(rot) 0.6045 | loss(pos) 0.0237 | loss(seq) 0.0589 | grad 2.6740 | lr 0.0000 | time_forward 3.6030 | time_backward 5.1720 |
[2023-10-22 21:08:08,705::train::INFO] [train] Iter 561999 | loss 1.6412 | loss(rot) 1.6101 | loss(pos) 0.0293 | loss(seq) 0.0018 | grad 32.7077 | lr 0.0000 | time_forward 3.8670 | time_backward 5.4320 |
[2023-10-22 21:08:17,584::train::INFO] [train] Iter 562000 | loss 0.4765 | loss(rot) 0.4431 | loss(pos) 0.0206 | loss(seq) 0.0128 | grad 1.3242 | lr 0.0000 | time_forward 3.7060 | time_backward 5.1700 |
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