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[2023-10-25 10:22:06,922::train::INFO] [train] Iter 594758 | loss 0.2772 | loss(rot) 0.0547 | loss(pos) 0.0315 | loss(seq) 0.1910 | grad 1.9606 | lr 0.0000 | time_forward 3.8710 | time_backward 5.5590 |
[2023-10-25 10:22:17,152::train::INFO] [train] Iter 594759 | loss 0.7034 | loss(rot) 0.4185 | loss(pos) 0.0592 | loss(seq) 0.2258 | grad 2.8753 | lr 0.0000 | time_forward 4.2810 | time_backward 5.9460 |
[2023-10-25 10:22:19,554::train::INFO] [train] Iter 594760 | loss 1.6090 | loss(rot) 1.1511 | loss(pos) 0.0867 | loss(seq) 0.3712 | grad 4.3203 | lr 0.0000 | time_forward 1.1300 | time_backward 1.2700 |
[2023-10-25 10:22:28,960::train::INFO] [train] Iter 594761 | loss 0.2698 | loss(rot) 0.0740 | loss(pos) 0.0274 | loss(seq) 0.1684 | grad 2.4169 | lr 0.0000 | time_forward 3.6150 | time_backward 5.7870 |
[2023-10-25 10:22:38,797::train::INFO] [train] Iter 594762 | loss 1.6853 | loss(rot) 1.2114 | loss(pos) 0.0619 | loss(seq) 0.4120 | grad 5.8478 | lr 0.0000 | time_forward 3.9980 | time_backward 5.8370 |
[2023-10-25 10:22:45,288::train::INFO] [train] Iter 594763 | loss 1.6445 | loss(rot) 1.5995 | loss(pos) 0.0450 | loss(seq) 0.0000 | grad 5.1772 | lr 0.0000 | time_forward 2.7510 | time_backward 3.7370 |
[2023-10-25 10:22:48,197::train::INFO] [train] Iter 594764 | loss 0.3523 | loss(rot) 0.0693 | loss(pos) 0.2568 | loss(seq) 0.0262 | grad 4.3573 | lr 0.0000 | time_forward 1.3340 | time_backward 1.5580 |
[2023-10-25 10:22:51,570::train::INFO] [train] Iter 594765 | loss 0.3054 | loss(rot) 0.2811 | loss(pos) 0.0243 | loss(seq) 0.0000 | grad 6.1420 | lr 0.0000 | time_forward 1.7460 | time_backward 1.6250 |
[2023-10-25 10:23:00,649::train::INFO] [train] Iter 594766 | loss 1.0309 | loss(rot) 0.3665 | loss(pos) 0.1462 | loss(seq) 0.5182 | grad 5.8974 | lr 0.0000 | time_forward 3.9570 | time_backward 5.1180 |
[2023-10-25 10:23:08,169::train::INFO] [train] Iter 594767 | loss 0.8883 | loss(rot) 0.2281 | loss(pos) 0.6566 | loss(seq) 0.0036 | grad 8.0662 | lr 0.0000 | time_forward 3.1320 | time_backward 4.3840 |
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