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[2023-09-02 19:37:26,811::train::INFO] [train] Iter 13573 | loss 1.6620 | loss(rot) 1.2566 | loss(pos) 0.1522 | loss(seq) 0.2532 | grad 5.3416 | lr 0.0010 | time_forward 2.7140 | time_backward 4.1790 |
[2023-09-02 19:37:35,429::train::INFO] [train] Iter 13574 | loss 0.9488 | loss(rot) 0.2983 | loss(pos) 0.5688 | loss(seq) 0.0817 | grad 2.9532 | lr 0.0010 | time_forward 3.4240 | time_backward 5.1900 |
[2023-09-02 19:37:42,603::train::INFO] [train] Iter 13575 | loss 1.0498 | loss(rot) 0.4770 | loss(pos) 0.1244 | loss(seq) 0.4483 | grad 4.0749 | lr 0.0010 | time_forward 2.8590 | time_backward 4.3130 |
[2023-09-02 19:37:50,881::train::INFO] [train] Iter 13576 | loss 1.1539 | loss(rot) 0.4300 | loss(pos) 0.4452 | loss(seq) 0.2787 | grad 4.2735 | lr 0.0010 | time_forward 3.3960 | time_backward 4.8780 |
[2023-09-02 19:37:57,866::train::INFO] [train] Iter 13577 | loss 1.9357 | loss(rot) 1.5014 | loss(pos) 0.2266 | loss(seq) 0.2077 | grad 4.8446 | lr 0.0010 | time_forward 3.0570 | time_backward 3.9070 |
[2023-09-02 19:38:04,909::train::INFO] [train] Iter 13578 | loss 1.1815 | loss(rot) 0.0787 | loss(pos) 1.0865 | loss(seq) 0.0163 | grad 5.7707 | lr 0.0010 | time_forward 3.0510 | time_backward 3.9880 |
[2023-09-02 19:38:12,436::train::INFO] [train] Iter 13579 | loss 0.7048 | loss(rot) 0.0459 | loss(pos) 0.6466 | loss(seq) 0.0122 | grad 4.3768 | lr 0.0010 | time_forward 3.3000 | time_backward 4.2240 |
[2023-09-02 19:38:14,686::train::INFO] [train] Iter 13580 | loss 0.7881 | loss(rot) 0.0440 | loss(pos) 0.7269 | loss(seq) 0.0173 | grad 4.7199 | lr 0.0010 | time_forward 1.0400 | time_backward 1.2050 |
[2023-09-02 19:38:22,303::train::INFO] [train] Iter 13581 | loss 1.1214 | loss(rot) 0.6862 | loss(pos) 0.3808 | loss(seq) 0.0545 | grad 3.6280 | lr 0.0010 | time_forward 3.2440 | time_backward 4.3700 |
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