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[2023-10-25 07:39:08,714::train::INFO] [train] Iter 593359 | loss 0.2500 | loss(rot) 0.2143 | loss(pos) 0.0351 | loss(seq) 0.0006 | grad 3.2151 | lr 0.0000 | time_forward 1.3390 | time_backward 1.4040 |
[2023-10-25 07:39:11,473::train::INFO] [train] Iter 593360 | loss 0.4393 | loss(rot) 0.1218 | loss(pos) 0.1055 | loss(seq) 0.2120 | grad 3.6542 | lr 0.0000 | time_forward 1.3490 | time_backward 1.4080 |
[2023-10-25 07:39:14,227::train::INFO] [train] Iter 593361 | loss 0.6862 | loss(rot) 0.3658 | loss(pos) 0.1536 | loss(seq) 0.1668 | grad 4.0833 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4150 |
[2023-10-25 07:39:20,174::train::INFO] [train] Iter 593362 | loss 0.1366 | loss(rot) 0.1226 | loss(pos) 0.0120 | loss(seq) 0.0020 | grad 2.1444 | lr 0.0000 | time_forward 2.5380 | time_backward 3.3820 |
[2023-10-25 07:39:29,253::train::INFO] [train] Iter 593363 | loss 0.7062 | loss(rot) 0.6338 | loss(pos) 0.0455 | loss(seq) 0.0270 | grad 3.3701 | lr 0.0000 | time_forward 3.7260 | time_backward 5.3420 |
[2023-10-25 07:39:38,256::train::INFO] [train] Iter 593364 | loss 0.2601 | loss(rot) 0.0978 | loss(pos) 0.0470 | loss(seq) 0.1152 | grad 2.6355 | lr 0.0000 | time_forward 3.7340 | time_backward 5.2650 |
[2023-10-25 07:39:40,991::train::INFO] [train] Iter 593365 | loss 0.1317 | loss(rot) 0.0586 | loss(pos) 0.0660 | loss(seq) 0.0071 | grad 2.2293 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4100 |
[2023-10-25 07:39:48,982::train::INFO] [train] Iter 593366 | loss 0.6675 | loss(rot) 0.0741 | loss(pos) 0.1405 | loss(seq) 0.4529 | grad 3.6966 | lr 0.0000 | time_forward 3.3970 | time_backward 4.5660 |
[2023-10-25 07:39:58,089::train::INFO] [train] Iter 593367 | loss 1.0599 | loss(rot) 0.5927 | loss(pos) 0.1114 | loss(seq) 0.3559 | grad 2.8992 | lr 0.0000 | time_forward 3.7440 | time_backward 5.3590 |
[2023-10-25 07:40:03,403::train::INFO] [train] Iter 593368 | loss 0.7558 | loss(rot) 0.0553 | loss(pos) 0.2197 | loss(seq) 0.4808 | grad 5.2408 | lr 0.0000 | time_forward 2.2820 | time_backward 3.0280 |
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