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[2023-10-25 09:57:48,352::train::INFO] [train] Iter 594558 | loss 0.4299 | loss(rot) 0.2437 | loss(pos) 0.0364 | loss(seq) 0.1497 | grad 2.5058 | lr 0.0000 | time_forward 4.1910 | time_backward 6.1760 |
[2023-10-25 09:57:57,909::train::INFO] [train] Iter 594559 | loss 0.9352 | loss(rot) 0.5499 | loss(pos) 0.0191 | loss(seq) 0.3663 | grad 1.8089 | lr 0.0000 | time_forward 3.9860 | time_backward 5.5680 |
[2023-10-25 09:58:06,070::train::INFO] [train] Iter 594560 | loss 0.5755 | loss(rot) 0.2884 | loss(pos) 0.1651 | loss(seq) 0.1220 | grad 4.7794 | lr 0.0000 | time_forward 3.4280 | time_backward 4.7300 |
[2023-10-25 09:58:16,012::train::INFO] [train] Iter 594561 | loss 0.5744 | loss(rot) 0.4454 | loss(pos) 0.0401 | loss(seq) 0.0889 | grad 2.4834 | lr 0.0000 | time_forward 4.0800 | time_backward 5.8580 |
[2023-10-25 09:58:19,312::train::INFO] [train] Iter 594562 | loss 0.5781 | loss(rot) 0.2742 | loss(pos) 0.1381 | loss(seq) 0.1657 | grad 2.4970 | lr 0.0000 | time_forward 1.4870 | time_backward 1.8020 |
[2023-10-25 09:58:25,870::train::INFO] [train] Iter 594563 | loss 0.8141 | loss(rot) 0.6202 | loss(pos) 0.0384 | loss(seq) 0.1555 | grad 2.8841 | lr 0.0000 | time_forward 2.7830 | time_backward 3.7620 |
[2023-10-25 09:58:28,686::train::INFO] [train] Iter 594564 | loss 0.4089 | loss(rot) 0.1018 | loss(pos) 0.0284 | loss(seq) 0.2788 | grad 2.3196 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4700 |
[2023-10-25 09:58:35,201::train::INFO] [train] Iter 594565 | loss 0.2125 | loss(rot) 0.0486 | loss(pos) 0.1556 | loss(seq) 0.0084 | grad 7.2416 | lr 0.0000 | time_forward 2.7220 | time_backward 3.7630 |
[2023-10-25 09:58:43,333::train::INFO] [train] Iter 594566 | loss 0.3193 | loss(rot) 0.2725 | loss(pos) 0.0300 | loss(seq) 0.0169 | grad 2.1601 | lr 0.0000 | time_forward 3.4140 | time_backward 4.7010 |
[2023-10-25 09:58:53,468::train::INFO] [train] Iter 594567 | loss 0.2795 | loss(rot) 0.0413 | loss(pos) 0.2079 | loss(seq) 0.0303 | grad 2.8902 | lr 0.0000 | time_forward 4.4130 | time_backward 5.7190 |
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