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[2023-10-23 21:35:42,988::train::INFO] [train] Iter 575677 | loss 0.4160 | loss(rot) 0.0388 | loss(pos) 0.3641 | loss(seq) 0.0131 | grad 3.6275 | lr 0.0000 | time_forward 3.4660 | time_backward 4.7880 |
[2023-10-23 21:35:52,010::train::INFO] [train] Iter 575678 | loss 0.5939 | loss(rot) 0.4116 | loss(pos) 0.0323 | loss(seq) 0.1501 | grad 4.0340 | lr 0.0000 | time_forward 3.8080 | time_backward 5.2120 |
[2023-10-23 21:36:00,301::train::INFO] [train] Iter 575679 | loss 2.1217 | loss(rot) 1.8328 | loss(pos) 0.0920 | loss(seq) 0.1970 | grad 7.3592 | lr 0.0000 | time_forward 3.4870 | time_backward 4.8010 |
[2023-10-23 21:36:03,163::train::INFO] [train] Iter 575680 | loss 0.2662 | loss(rot) 0.0904 | loss(pos) 0.0654 | loss(seq) 0.1104 | grad 2.3119 | lr 0.0000 | time_forward 1.3240 | time_backward 1.5340 |
[2023-10-23 21:36:06,082::train::INFO] [train] Iter 575681 | loss 1.0354 | loss(rot) 0.8183 | loss(pos) 0.0286 | loss(seq) 0.1885 | grad 4.2714 | lr 0.0000 | time_forward 1.3930 | time_backward 1.5230 |
[2023-10-23 21:36:14,529::train::INFO] [train] Iter 575682 | loss 0.5454 | loss(rot) 0.3768 | loss(pos) 0.0271 | loss(seq) 0.1415 | grad 4.3270 | lr 0.0000 | time_forward 3.5720 | time_backward 4.8730 |
[2023-10-23 21:36:23,351::train::INFO] [train] Iter 575683 | loss 0.7086 | loss(rot) 0.5560 | loss(pos) 0.0282 | loss(seq) 0.1244 | grad 3.8776 | lr 0.0000 | time_forward 3.7520 | time_backward 5.0660 |
[2023-10-23 21:36:26,229::train::INFO] [train] Iter 575684 | loss 0.2585 | loss(rot) 0.0564 | loss(pos) 0.1423 | loss(seq) 0.0598 | grad 3.8874 | lr 0.0000 | time_forward 1.3500 | time_backward 1.5250 |
[2023-10-23 21:36:34,703::train::INFO] [train] Iter 575685 | loss 1.0575 | loss(rot) 1.0268 | loss(pos) 0.0216 | loss(seq) 0.0091 | grad 2.4332 | lr 0.0000 | time_forward 3.5730 | time_backward 4.8970 |
[2023-10-23 21:36:43,528::train::INFO] [train] Iter 575686 | loss 1.7524 | loss(rot) 1.0506 | loss(pos) 0.3332 | loss(seq) 0.3686 | grad 3.7320 | lr 0.0000 | time_forward 3.7050 | time_backward 5.1160 |
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