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[2023-09-02 08:32:05,519::train::INFO] [train] Iter 07978 | loss 1.6146 | loss(rot) 0.9640 | loss(pos) 0.1748 | loss(seq) 0.4759 | grad 4.4668 | lr 0.0010 | time_forward 1.1180 | time_backward 1.2080 |
[2023-09-02 08:32:08,932::train::INFO] [train] Iter 07979 | loss 2.0839 | loss(rot) 1.2429 | loss(pos) 0.3995 | loss(seq) 0.4414 | grad 5.6221 | lr 0.0010 | time_forward 1.4060 | time_backward 2.0040 |
[2023-09-02 08:32:11,653::train::INFO] [train] Iter 07980 | loss 2.5178 | loss(rot) 2.1083 | loss(pos) 0.1812 | loss(seq) 0.2283 | grad 4.7268 | lr 0.0010 | time_forward 1.2510 | time_backward 1.4550 |
[2023-09-02 08:32:20,485::train::INFO] [train] Iter 07981 | loss 1.7637 | loss(rot) 1.2294 | loss(pos) 0.1558 | loss(seq) 0.3786 | grad 4.7319 | lr 0.0010 | time_forward 3.7250 | time_backward 5.1050 |
[2023-09-02 08:32:27,617::train::INFO] [train] Iter 07982 | loss 2.3668 | loss(rot) 2.2123 | loss(pos) 0.1544 | loss(seq) 0.0001 | grad 3.4663 | lr 0.0010 | time_forward 3.0680 | time_backward 4.0600 |
[2023-09-02 08:32:36,229::train::INFO] [train] Iter 07983 | loss 2.0365 | loss(rot) 1.5624 | loss(pos) 0.1691 | loss(seq) 0.3049 | grad 4.2585 | lr 0.0010 | time_forward 3.6640 | time_backward 4.9440 |
[2023-09-02 08:32:46,169::train::INFO] [train] Iter 07984 | loss 1.3718 | loss(rot) 0.1960 | loss(pos) 1.1633 | loss(seq) 0.0125 | grad 5.8485 | lr 0.0010 | time_forward 4.0390 | time_backward 5.8980 |
[2023-09-02 08:32:56,091::train::INFO] [train] Iter 07985 | loss 1.5681 | loss(rot) 0.9065 | loss(pos) 0.1673 | loss(seq) 0.4943 | grad 3.2869 | lr 0.0010 | time_forward 4.0810 | time_backward 5.8390 |
[2023-09-02 08:33:04,458::train::INFO] [train] Iter 07986 | loss 1.5732 | loss(rot) 1.0382 | loss(pos) 0.2192 | loss(seq) 0.3158 | grad 5.8357 | lr 0.0010 | time_forward 3.4750 | time_backward 4.8890 |
[2023-09-02 08:33:14,249::train::INFO] [train] Iter 07987 | loss 0.9927 | loss(rot) 0.2578 | loss(pos) 0.3871 | loss(seq) 0.3478 | grad 2.8530 | lr 0.0010 | time_forward 4.0500 | time_backward 5.7370 |
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