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[2023-09-02 18:51:34,237::train::INFO] [train] Iter 13172 | loss 1.7644 | loss(rot) 1.6112 | loss(pos) 0.1511 | loss(seq) 0.0020 | grad 7.0759 | lr 0.0010 | time_forward 3.5260 | time_backward 4.4570 |
[2023-09-02 18:51:36,370::train::INFO] [train] Iter 13173 | loss 1.0549 | loss(rot) 0.8758 | loss(pos) 0.1779 | loss(seq) 0.0012 | grad 6.3007 | lr 0.0010 | time_forward 0.9790 | time_backward 1.1500 |
[2023-09-02 18:51:43,913::train::INFO] [train] Iter 13174 | loss 1.9231 | loss(rot) 1.4902 | loss(pos) 0.1296 | loss(seq) 0.3032 | grad 18.7770 | lr 0.0010 | time_forward 3.2240 | time_backward 4.3160 |
[2023-09-02 18:51:46,631::train::INFO] [train] Iter 13175 | loss 0.9868 | loss(rot) 0.2277 | loss(pos) 0.5619 | loss(seq) 0.1972 | grad 4.4851 | lr 0.0010 | time_forward 1.2320 | time_backward 1.4830 |
[2023-09-02 18:51:49,804::train::INFO] [train] Iter 13176 | loss 3.6676 | loss(rot) 0.0054 | loss(pos) 3.6622 | loss(seq) 0.0000 | grad 4.9368 | lr 0.0010 | time_forward 1.3840 | time_backward 1.7850 |
[2023-09-02 18:51:57,554::train::INFO] [train] Iter 13177 | loss 1.1332 | loss(rot) 0.2869 | loss(pos) 0.3796 | loss(seq) 0.4667 | grad 5.4978 | lr 0.0010 | time_forward 3.0220 | time_backward 4.7260 |
[2023-09-02 18:52:06,600::train::INFO] [train] Iter 13178 | loss 2.0115 | loss(rot) 1.8319 | loss(pos) 0.1784 | loss(seq) 0.0012 | grad 5.1725 | lr 0.0010 | time_forward 3.4950 | time_backward 5.5470 |
[2023-09-02 18:52:15,169::train::INFO] [train] Iter 13179 | loss 1.9632 | loss(rot) 1.2354 | loss(pos) 0.2387 | loss(seq) 0.4892 | grad 8.5382 | lr 0.0010 | time_forward 3.5480 | time_backward 5.0170 |
[2023-09-02 18:52:24,908::train::INFO] [train] Iter 13180 | loss 1.9009 | loss(rot) 1.1462 | loss(pos) 0.2373 | loss(seq) 0.5173 | grad 4.9331 | lr 0.0010 | time_forward 3.8850 | time_backward 5.8520 |
[2023-09-02 18:52:33,169::train::INFO] [train] Iter 13181 | loss 1.3899 | loss(rot) 0.7794 | loss(pos) 0.1977 | loss(seq) 0.4129 | grad 3.5723 | lr 0.0010 | time_forward 3.4880 | time_backward 4.7690 |
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