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[2023-10-25 06:02:17,786::train::INFO] [train] Iter 592460 | loss 0.2744 | loss(rot) 0.1022 | loss(pos) 0.1135 | loss(seq) 0.0586 | grad 3.2835 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4070 |
[2023-10-25 06:02:25,597::train::INFO] [train] Iter 592461 | loss 0.7019 | loss(rot) 0.4497 | loss(pos) 0.0424 | loss(seq) 0.2098 | grad 3.2818 | lr 0.0000 | time_forward 3.3430 | time_backward 4.4490 |
[2023-10-25 06:02:32,879::train::INFO] [train] Iter 592462 | loss 0.1530 | loss(rot) 0.1022 | loss(pos) 0.0082 | loss(seq) 0.0427 | grad 1.7851 | lr 0.0000 | time_forward 3.0420 | time_backward 4.2360 |
[2023-10-25 06:02:38,067::train::INFO] [train] Iter 592463 | loss 0.1903 | loss(rot) 0.1205 | loss(pos) 0.0432 | loss(seq) 0.0265 | grad 2.6414 | lr 0.0000 | time_forward 2.2660 | time_backward 2.9190 |
[2023-10-25 06:02:40,674::train::INFO] [train] Iter 592464 | loss 0.3507 | loss(rot) 0.0442 | loss(pos) 0.0219 | loss(seq) 0.2846 | grad 2.1167 | lr 0.0000 | time_forward 1.2170 | time_backward 1.3850 |
[2023-10-25 06:02:49,557::train::INFO] [train] Iter 592465 | loss 0.2125 | loss(rot) 0.0629 | loss(pos) 0.1293 | loss(seq) 0.0203 | grad 2.9232 | lr 0.0000 | time_forward 3.6320 | time_backward 5.2190 |
[2023-10-25 06:02:57,461::train::INFO] [train] Iter 592466 | loss 0.3396 | loss(rot) 0.0360 | loss(pos) 0.0860 | loss(seq) 0.2176 | grad 2.2755 | lr 0.0000 | time_forward 3.3960 | time_backward 4.5050 |
[2023-10-25 06:03:05,621::train::INFO] [train] Iter 592467 | loss 0.6648 | loss(rot) 0.5371 | loss(pos) 0.0266 | loss(seq) 0.1012 | grad 3.6386 | lr 0.0000 | time_forward 3.5170 | time_backward 4.6390 |
[2023-10-25 06:03:13,010::train::INFO] [train] Iter 592468 | loss 0.1613 | loss(rot) 0.0863 | loss(pos) 0.0313 | loss(seq) 0.0437 | grad 2.0239 | lr 0.0000 | time_forward 3.1410 | time_backward 4.2440 |
[2023-10-25 06:03:20,698::train::INFO] [train] Iter 592469 | loss 0.2214 | loss(rot) 0.0727 | loss(pos) 0.0125 | loss(seq) 0.1362 | grad 1.9402 | lr 0.0000 | time_forward 3.2970 | time_backward 4.3890 |
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