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[2023-09-02 13:38:19,444::train::INFO] [train] Iter 10475 | loss 3.2720 | loss(rot) 2.5620 | loss(pos) 0.2404 | loss(seq) 0.4696 | grad 6.3202 | lr 0.0010 | time_forward 1.2280 | time_backward 1.5090 |
[2023-09-02 13:38:29,406::train::INFO] [train] Iter 10476 | loss 1.6982 | loss(rot) 1.4358 | loss(pos) 0.1829 | loss(seq) 0.0795 | grad 4.5115 | lr 0.0010 | time_forward 4.1270 | time_backward 5.8320 |
[2023-09-02 13:38:37,535::train::INFO] [train] Iter 10477 | loss 1.6734 | loss(rot) 0.7297 | loss(pos) 0.5781 | loss(seq) 0.3657 | grad 4.4635 | lr 0.0010 | time_forward 3.2680 | time_backward 4.8570 |
[2023-09-02 13:38:39,739::train::INFO] [train] Iter 10478 | loss 0.9698 | loss(rot) 0.7539 | loss(pos) 0.2148 | loss(seq) 0.0012 | grad 5.3034 | lr 0.0010 | time_forward 1.0190 | time_backward 1.1820 |
[2023-09-02 13:38:45,766::train::INFO] [train] Iter 10479 | loss 1.9030 | loss(rot) 1.7770 | loss(pos) 0.1192 | loss(seq) 0.0068 | grad 6.0717 | lr 0.0010 | time_forward 2.5470 | time_backward 3.4760 |
[2023-09-02 13:38:48,991::train::INFO] [train] Iter 10480 | loss 1.7190 | loss(rot) 0.8562 | loss(pos) 0.4548 | loss(seq) 0.4079 | grad 2.8979 | lr 0.0010 | time_forward 1.4390 | time_backward 1.7820 |
[2023-09-02 13:38:59,135::train::INFO] [train] Iter 10481 | loss 0.8490 | loss(rot) 0.2001 | loss(pos) 0.3912 | loss(seq) 0.2576 | grad 3.9668 | lr 0.0010 | time_forward 4.1200 | time_backward 6.0070 |
[2023-09-02 13:39:02,658::train::INFO] [train] Iter 10482 | loss 1.0010 | loss(rot) 0.1114 | loss(pos) 0.8721 | loss(seq) 0.0175 | grad 5.7752 | lr 0.0010 | time_forward 1.4360 | time_backward 2.0820 |
[2023-09-02 13:39:11,776::train::INFO] [train] Iter 10483 | loss 1.9411 | loss(rot) 0.3835 | loss(pos) 1.3117 | loss(seq) 0.2459 | grad 6.0563 | lr 0.0010 | time_forward 3.8190 | time_backward 5.2950 |
[2023-09-02 13:39:20,895::train::INFO] [train] Iter 10484 | loss 1.3563 | loss(rot) 1.2194 | loss(pos) 0.0461 | loss(seq) 0.0908 | grad 4.9410 | lr 0.0010 | time_forward 3.8590 | time_backward 5.2570 |
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