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[2023-09-02 19:49:07,268::train::INFO] [train] Iter 13672 | loss 1.2643 | loss(rot) 0.4157 | loss(pos) 0.4884 | loss(seq) 0.3603 | grad 5.9217 | lr 0.0010 | time_forward 1.2770 | time_backward 1.4500 |
[2023-09-02 19:49:10,027::train::INFO] [train] Iter 13673 | loss 2.0963 | loss(rot) 1.9590 | loss(pos) 0.0847 | loss(seq) 0.0526 | grad 5.5171 | lr 0.0010 | time_forward 1.3160 | time_backward 1.4390 |
[2023-09-02 19:49:19,092::train::INFO] [train] Iter 13674 | loss 0.5215 | loss(rot) 0.4063 | loss(pos) 0.0783 | loss(seq) 0.0368 | grad 3.4153 | lr 0.0010 | time_forward 3.6480 | time_backward 5.4130 |
[2023-09-02 19:49:29,308::train::INFO] [train] Iter 13675 | loss 1.4516 | loss(rot) 1.0908 | loss(pos) 0.1126 | loss(seq) 0.2482 | grad 4.0859 | lr 0.0010 | time_forward 4.1460 | time_backward 6.0670 |
[2023-09-02 19:49:32,101::train::INFO] [train] Iter 13676 | loss 1.4631 | loss(rot) 0.5480 | loss(pos) 0.3970 | loss(seq) 0.5181 | grad 4.7376 | lr 0.0010 | time_forward 1.2940 | time_backward 1.4950 |
[2023-09-02 19:49:42,087::train::INFO] [train] Iter 13677 | loss 2.9938 | loss(rot) 0.0239 | loss(pos) 2.9699 | loss(seq) 0.0000 | grad 7.1496 | lr 0.0010 | time_forward 4.1000 | time_backward 5.8820 |
[2023-09-02 19:49:52,032::train::INFO] [train] Iter 13678 | loss 1.5225 | loss(rot) 0.0344 | loss(pos) 1.4830 | loss(seq) 0.0050 | grad 5.8230 | lr 0.0010 | time_forward 3.9480 | time_backward 5.9930 |
[2023-09-02 19:50:00,892::train::INFO] [train] Iter 13679 | loss 1.7420 | loss(rot) 1.3919 | loss(pos) 0.0650 | loss(seq) 0.2850 | grad 4.0898 | lr 0.0010 | time_forward 3.5020 | time_backward 5.3550 |
[2023-09-02 19:50:04,348::train::INFO] [train] Iter 13680 | loss 1.6834 | loss(rot) 1.5920 | loss(pos) 0.0907 | loss(seq) 0.0007 | grad 4.9226 | lr 0.0010 | time_forward 1.4350 | time_backward 2.0170 |
[2023-09-02 19:50:07,703::train::INFO] [train] Iter 13681 | loss 1.2295 | loss(rot) 0.4587 | loss(pos) 0.4752 | loss(seq) 0.2955 | grad 3.4439 | lr 0.0010 | time_forward 1.4880 | time_backward 1.8640 |
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