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[2023-09-02 09:59:30,992::train::INFO] [train] Iter 08677 | loss 1.3855 | loss(rot) 1.0127 | loss(pos) 0.1281 | loss(seq) 0.2448 | grad 4.5142 | lr 0.0010 | time_forward 3.8780 | time_backward 5.0150 |
[2023-09-02 09:59:33,707::train::INFO] [train] Iter 08678 | loss 1.1721 | loss(rot) 0.1714 | loss(pos) 0.7145 | loss(seq) 0.2862 | grad 5.1249 | lr 0.0010 | time_forward 1.2610 | time_backward 1.4500 |
[2023-09-02 09:59:42,475::train::INFO] [train] Iter 08679 | loss 0.4292 | loss(rot) 0.1376 | loss(pos) 0.2434 | loss(seq) 0.0482 | grad 3.7110 | lr 0.0010 | time_forward 3.8020 | time_backward 4.9630 |
[2023-09-02 09:59:45,332::train::INFO] [train] Iter 08680 | loss 1.9287 | loss(rot) 0.7573 | loss(pos) 0.7072 | loss(seq) 0.4643 | grad 6.1909 | lr 0.0010 | time_forward 1.3860 | time_backward 1.4690 |
[2023-09-02 09:59:47,422::train::INFO] [train] Iter 08681 | loss 1.0263 | loss(rot) 0.2594 | loss(pos) 0.4240 | loss(seq) 0.3428 | grad 4.8475 | lr 0.0010 | time_forward 0.9980 | time_backward 1.0880 |
[2023-09-02 09:59:55,529::train::INFO] [train] Iter 08682 | loss 1.7739 | loss(rot) 1.6220 | loss(pos) 0.0771 | loss(seq) 0.0748 | grad 4.0348 | lr 0.0010 | time_forward 3.4790 | time_backward 4.6240 |
[2023-09-02 10:00:03,993::train::INFO] [train] Iter 08683 | loss 3.0129 | loss(rot) 2.7452 | loss(pos) 0.2676 | loss(seq) 0.0000 | grad 4.4673 | lr 0.0010 | time_forward 3.5520 | time_backward 4.9080 |
[2023-09-02 10:00:14,042::train::INFO] [train] Iter 08684 | loss 2.2307 | loss(rot) 1.8023 | loss(pos) 0.0934 | loss(seq) 0.3350 | grad 3.5982 | lr 0.0010 | time_forward 4.2810 | time_backward 5.7660 |
[2023-09-02 10:00:22,674::train::INFO] [train] Iter 08685 | loss 1.2417 | loss(rot) 0.5631 | loss(pos) 0.1855 | loss(seq) 0.4931 | grad 4.0545 | lr 0.0010 | time_forward 3.6440 | time_backward 4.9850 |
[2023-09-02 10:00:31,029::train::INFO] [train] Iter 08686 | loss 1.5159 | loss(rot) 1.4633 | loss(pos) 0.0525 | loss(seq) 0.0001 | grad 5.0036 | lr 0.0010 | time_forward 3.4760 | time_backward 4.8750 |
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