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[2023-10-23 11:35:13,240::train::INFO] [train] Iter 570282 | loss 0.7622 | loss(rot) 0.6563 | loss(pos) 0.0199 | loss(seq) 0.0860 | grad 4.8246 | lr 0.0000 | time_forward 3.2810 | time_backward 4.6320 |
[2023-10-23 11:35:19,974::train::INFO] [train] Iter 570283 | loss 0.6730 | loss(rot) 0.4651 | loss(pos) 0.0980 | loss(seq) 0.1099 | grad 12.4681 | lr 0.0000 | time_forward 2.8810 | time_backward 3.8490 |
[2023-10-23 11:35:26,967::train::INFO] [train] Iter 570284 | loss 0.2436 | loss(rot) 0.0320 | loss(pos) 0.2072 | loss(seq) 0.0043 | grad 4.5716 | lr 0.0000 | time_forward 3.0870 | time_backward 3.9030 |
[2023-10-23 11:35:30,007::train::INFO] [train] Iter 570285 | loss 0.6831 | loss(rot) 0.2755 | loss(pos) 0.0730 | loss(seq) 0.3346 | grad 2.9629 | lr 0.0000 | time_forward 1.3840 | time_backward 1.6540 |
[2023-10-23 11:35:32,686::train::INFO] [train] Iter 570286 | loss 0.1689 | loss(rot) 0.0688 | loss(pos) 0.0808 | loss(seq) 0.0193 | grad 2.6198 | lr 0.0000 | time_forward 1.2650 | time_backward 1.4020 |
[2023-10-23 11:35:35,318::train::INFO] [train] Iter 570287 | loss 0.2828 | loss(rot) 0.2229 | loss(pos) 0.0172 | loss(seq) 0.0427 | grad 2.2460 | lr 0.0000 | time_forward 1.2540 | time_backward 1.3750 |
[2023-10-23 11:35:42,216::train::INFO] [train] Iter 570288 | loss 0.2674 | loss(rot) 0.1851 | loss(pos) 0.0323 | loss(seq) 0.0500 | grad 2.1632 | lr 0.0000 | time_forward 2.9370 | time_backward 3.9390 |
[2023-10-23 11:35:44,920::train::INFO] [train] Iter 570289 | loss 0.2298 | loss(rot) 0.1109 | loss(pos) 0.0152 | loss(seq) 0.1038 | grad 3.5760 | lr 0.0000 | time_forward 1.2950 | time_backward 1.4060 |
[2023-10-23 11:35:53,188::train::INFO] [train] Iter 570290 | loss 0.2522 | loss(rot) 0.2020 | loss(pos) 0.0499 | loss(seq) 0.0002 | grad 7.7942 | lr 0.0000 | time_forward 3.4280 | time_backward 4.8170 |
[2023-10-23 11:35:55,887::train::INFO] [train] Iter 570291 | loss 1.6242 | loss(rot) 1.1623 | loss(pos) 0.0855 | loss(seq) 0.3763 | grad 7.8702 | lr 0.0000 | time_forward 1.2710 | time_backward 1.4260 |
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