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[2023-09-02 14:15:33,468::train::INFO] [train] Iter 10775 | loss 1.5523 | loss(rot) 0.8277 | loss(pos) 0.2009 | loss(seq) 0.5238 | grad 2.7276 | lr 0.0010 | time_forward 4.0710 | time_backward 5.7600 |
[2023-09-02 14:15:36,372::train::INFO] [train] Iter 10776 | loss 2.4306 | loss(rot) 2.2751 | loss(pos) 0.1555 | loss(seq) 0.0000 | grad 6.0475 | lr 0.0010 | time_forward 1.2920 | time_backward 1.6080 |
[2023-09-02 14:15:38,729::train::INFO] [train] Iter 10777 | loss 2.2332 | loss(rot) 1.4556 | loss(pos) 0.2651 | loss(seq) 0.5125 | grad 6.0302 | lr 0.0010 | time_forward 1.1290 | time_backward 1.2260 |
[2023-09-02 14:15:49,281::train::INFO] [train] Iter 10778 | loss 1.0939 | loss(rot) 0.8925 | loss(pos) 0.2013 | loss(seq) 0.0001 | grad 3.4851 | lr 0.0010 | time_forward 4.4800 | time_backward 6.0670 |
[2023-09-02 14:16:00,369::train::INFO] [train] Iter 10779 | loss 1.4680 | loss(rot) 0.7390 | loss(pos) 0.2248 | loss(seq) 0.5041 | grad 4.0402 | lr 0.0010 | time_forward 4.0760 | time_backward 7.0080 |
[2023-09-02 14:16:09,723::train::INFO] [train] Iter 10780 | loss 1.3057 | loss(rot) 0.6925 | loss(pos) 0.2015 | loss(seq) 0.4117 | grad 4.3106 | lr 0.0010 | time_forward 3.8320 | time_backward 5.5190 |
[2023-09-02 14:16:12,536::train::INFO] [train] Iter 10781 | loss 1.7928 | loss(rot) 1.6131 | loss(pos) 0.0831 | loss(seq) 0.0967 | grad 10.4371 | lr 0.0010 | time_forward 1.3330 | time_backward 1.4760 |
[2023-09-02 14:16:23,730::train::INFO] [train] Iter 10782 | loss 1.2143 | loss(rot) 0.5675 | loss(pos) 0.3926 | loss(seq) 0.2542 | grad 3.3782 | lr 0.0010 | time_forward 4.5910 | time_backward 6.6000 |
[2023-09-02 14:16:36,489::train::INFO] [train] Iter 10783 | loss 1.8417 | loss(rot) 1.5102 | loss(pos) 0.1008 | loss(seq) 0.2306 | grad 11.7180 | lr 0.0010 | time_forward 4.7350 | time_backward 8.0200 |
[2023-09-02 14:16:46,478::train::INFO] [train] Iter 10784 | loss 2.7460 | loss(rot) 1.8009 | loss(pos) 0.3437 | loss(seq) 0.6014 | grad 6.5854 | lr 0.0010 | time_forward 4.1900 | time_backward 5.7950 |
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