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[2023-10-22 22:04:57,365::train::INFO] [train] Iter 562490 | loss 0.9121 | loss(rot) 0.6715 | loss(pos) 0.0333 | loss(seq) 0.2073 | grad 4.6147 | lr 0.0000 | time_forward 2.5930 | time_backward 3.4420 |
[2023-10-22 22:05:18,493::train::INFO] [train] Iter 562491 | loss 0.1757 | loss(rot) 0.1271 | loss(pos) 0.0485 | loss(seq) 0.0001 | grad 2.7811 | lr 0.0000 | time_forward 4.4790 | time_backward 16.6460 |
[2023-10-22 22:05:45,071::train::INFO] [train] Iter 562492 | loss 1.4553 | loss(rot) 1.3808 | loss(pos) 0.0438 | loss(seq) 0.0307 | grad 6.2289 | lr 0.0000 | time_forward 21.8010 | time_backward 4.7740 |
[2023-10-22 22:06:03,125::train::INFO] [train] Iter 562493 | loss 0.7996 | loss(rot) 0.6933 | loss(pos) 0.0661 | loss(seq) 0.0402 | grad 2.6093 | lr 0.0000 | time_forward 12.4270 | time_backward 5.6230 |
[2023-10-22 22:06:16,854::train::INFO] [train] Iter 562494 | loss 0.9662 | loss(rot) 0.1992 | loss(pos) 0.4122 | loss(seq) 0.3547 | grad 3.4041 | lr 0.0000 | time_forward 7.8310 | time_backward 5.8960 |
[2023-10-22 22:06:24,446::train::INFO] [train] Iter 562495 | loss 1.7257 | loss(rot) 1.4595 | loss(pos) 0.0751 | loss(seq) 0.1911 | grad 7.9857 | lr 0.0000 | time_forward 3.3090 | time_backward 4.2790 |
[2023-10-22 22:06:32,424::train::INFO] [train] Iter 562496 | loss 0.5910 | loss(rot) 0.4693 | loss(pos) 0.0222 | loss(seq) 0.0995 | grad 3.0824 | lr 0.0000 | time_forward 3.2440 | time_backward 4.7320 |
[2023-10-22 22:06:39,401::train::INFO] [train] Iter 562497 | loss 1.2609 | loss(rot) 0.5610 | loss(pos) 0.3096 | loss(seq) 0.3903 | grad 5.5396 | lr 0.0000 | time_forward 3.0430 | time_backward 3.9300 |
[2023-10-22 22:06:45,411::train::INFO] [train] Iter 562498 | loss 0.6773 | loss(rot) 0.3646 | loss(pos) 0.0413 | loss(seq) 0.2715 | grad 4.3420 | lr 0.0000 | time_forward 2.5230 | time_backward 3.4840 |
[2023-10-22 22:07:05,079::train::INFO] [train] Iter 562499 | loss 0.9578 | loss(rot) 0.7672 | loss(pos) 0.0409 | loss(seq) 0.1497 | grad 3.7787 | lr 0.0000 | time_forward 15.0480 | time_backward 4.6170 |
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