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[2023-10-25 19:11:56,517::train::INFO] [train] Iter 599354 | loss 0.4733 | loss(rot) 0.0746 | loss(pos) 0.0335 | loss(seq) 0.3652 | grad 1.9864 | lr 0.0000 | time_forward 3.5770 | time_backward 4.9360 |
[2023-10-25 19:12:06,675::train::INFO] [train] Iter 599355 | loss 0.7235 | loss(rot) 0.6700 | loss(pos) 0.0536 | loss(seq) 0.0000 | grad 3.2254 | lr 0.0000 | time_forward 4.2030 | time_backward 5.9520 |
[2023-10-25 19:12:09,455::train::INFO] [train] Iter 599356 | loss 0.5147 | loss(rot) 0.4830 | loss(pos) 0.0310 | loss(seq) 0.0008 | grad 1.3816 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4450 |
[2023-10-25 19:12:19,633::train::INFO] [train] Iter 599357 | loss 0.7813 | loss(rot) 0.5605 | loss(pos) 0.1038 | loss(seq) 0.1170 | grad 3.8633 | lr 0.0000 | time_forward 4.0920 | time_backward 6.0490 |
[2023-10-25 19:12:28,275::train::INFO] [train] Iter 599358 | loss 1.1594 | loss(rot) 0.0114 | loss(pos) 1.1462 | loss(seq) 0.0019 | grad 16.5991 | lr 0.0000 | time_forward 3.6600 | time_backward 4.9780 |
[2023-10-25 19:12:37,470::train::INFO] [train] Iter 599359 | loss 0.1531 | loss(rot) 0.0750 | loss(pos) 0.0189 | loss(seq) 0.0591 | grad 1.8420 | lr 0.0000 | time_forward 3.9060 | time_backward 5.2860 |
[2023-10-25 19:12:47,768::train::INFO] [train] Iter 599360 | loss 0.4655 | loss(rot) 0.3687 | loss(pos) 0.0139 | loss(seq) 0.0829 | grad 2.9379 | lr 0.0000 | time_forward 4.3310 | time_backward 5.9630 |
[2023-10-25 19:12:50,712::train::INFO] [train] Iter 599361 | loss 0.5332 | loss(rot) 0.1452 | loss(pos) 0.0796 | loss(seq) 0.3083 | grad 2.8924 | lr 0.0000 | time_forward 1.4570 | time_backward 1.4840 |
[2023-10-25 19:13:00,603::train::INFO] [train] Iter 599362 | loss 1.2690 | loss(rot) 0.5249 | loss(pos) 0.4820 | loss(seq) 0.2621 | grad 3.2599 | lr 0.0000 | time_forward 4.0400 | time_backward 5.8480 |
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