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[2023-09-02 16:01:54,013::train::INFO] [train] Iter 11674 | loss 2.6859 | loss(rot) 2.3713 | loss(pos) 0.2342 | loss(seq) 0.0804 | grad 7.7288 | lr 0.0010 | time_forward 3.0140 | time_backward 4.1000 |
[2023-09-02 16:02:02,654::train::INFO] [train] Iter 11675 | loss 0.9748 | loss(rot) 0.3542 | loss(pos) 0.3897 | loss(seq) 0.2310 | grad 3.1507 | lr 0.0010 | time_forward 3.4920 | time_backward 5.1460 |
[2023-09-02 16:02:11,075::train::INFO] [train] Iter 11676 | loss 1.9474 | loss(rot) 0.0404 | loss(pos) 1.6008 | loss(seq) 0.3062 | grad 6.6421 | lr 0.0010 | time_forward 3.4860 | time_backward 4.9320 |
[2023-09-02 16:02:19,653::train::INFO] [train] Iter 11677 | loss 1.9570 | loss(rot) 1.6033 | loss(pos) 0.1057 | loss(seq) 0.2480 | grad 6.0610 | lr 0.0010 | time_forward 3.5410 | time_backward 5.0320 |
[2023-09-02 16:02:22,313::train::INFO] [train] Iter 11678 | loss 1.6901 | loss(rot) 0.4481 | loss(pos) 0.7524 | loss(seq) 0.4896 | grad 5.9239 | lr 0.0010 | time_forward 1.2060 | time_backward 1.4510 |
[2023-09-02 16:02:29,709::train::INFO] [train] Iter 11679 | loss 1.4917 | loss(rot) 0.9606 | loss(pos) 0.1160 | loss(seq) 0.4151 | grad 3.8551 | lr 0.0010 | time_forward 3.0110 | time_backward 4.3810 |
[2023-09-02 16:02:37,673::train::INFO] [train] Iter 11680 | loss 1.6106 | loss(rot) 0.7517 | loss(pos) 0.5140 | loss(seq) 0.3449 | grad 3.8766 | lr 0.0010 | time_forward 3.2350 | time_backward 4.7260 |
[2023-09-02 16:02:40,659::train::INFO] [train] Iter 11681 | loss 1.1829 | loss(rot) 0.0596 | loss(pos) 1.1203 | loss(seq) 0.0030 | grad 5.8725 | lr 0.0010 | time_forward 1.3050 | time_backward 1.6780 |
[2023-09-02 16:02:48,956::train::INFO] [train] Iter 11682 | loss 1.9694 | loss(rot) 0.0133 | loss(pos) 1.9545 | loss(seq) 0.0016 | grad 9.1399 | lr 0.0010 | time_forward 3.1940 | time_backward 5.1000 |
[2023-09-02 16:02:55,767::train::INFO] [train] Iter 11683 | loss 1.2844 | loss(rot) 0.3889 | loss(pos) 0.2272 | loss(seq) 0.6683 | grad 5.4940 | lr 0.0010 | time_forward 2.7840 | time_backward 4.0230 |
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