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[2023-09-01 23:13:15,857::train::INFO] [train] Iter 03382 | loss 1.4569 | loss(rot) 0.6993 | loss(pos) 0.6524 | loss(seq) 0.1052 | grad 5.3601 | lr 0.0010 | time_forward 2.0460 | time_backward 2.7910 |
[2023-09-01 23:13:24,103::train::INFO] [train] Iter 03383 | loss 1.7508 | loss(rot) 0.5958 | loss(pos) 0.5841 | loss(seq) 0.5709 | grad 4.1587 | lr 0.0010 | time_forward 3.4450 | time_backward 4.7980 |
[2023-09-01 23:13:26,836::train::INFO] [train] Iter 03384 | loss 1.1580 | loss(rot) 0.0974 | loss(pos) 1.0496 | loss(seq) 0.0110 | grad 6.5619 | lr 0.0010 | time_forward 1.2320 | time_backward 1.4450 |
[2023-09-01 23:13:37,059::train::INFO] [train] Iter 03385 | loss 1.1034 | loss(rot) 0.3190 | loss(pos) 0.4904 | loss(seq) 0.2941 | grad 3.2971 | lr 0.0010 | time_forward 4.1300 | time_backward 6.0890 |
[2023-09-01 23:13:45,502::train::INFO] [train] Iter 03386 | loss 1.0889 | loss(rot) 0.2784 | loss(pos) 0.2096 | loss(seq) 0.6009 | grad 3.9148 | lr 0.0010 | time_forward 3.5300 | time_backward 4.9090 |
[2023-09-01 23:13:53,591::train::INFO] [train] Iter 03387 | loss 1.4183 | loss(rot) 0.5295 | loss(pos) 0.8258 | loss(seq) 0.0629 | grad 6.1714 | lr 0.0010 | time_forward 3.4650 | time_backward 4.6200 |
[2023-09-01 23:14:02,880::train::INFO] [train] Iter 03388 | loss 2.7905 | loss(rot) 2.1212 | loss(pos) 0.3645 | loss(seq) 0.3048 | grad 7.4716 | lr 0.0010 | time_forward 3.8870 | time_backward 5.3980 |
[2023-09-01 23:14:12,971::train::INFO] [train] Iter 03389 | loss 1.8367 | loss(rot) 0.9888 | loss(pos) 0.4318 | loss(seq) 0.4161 | grad 6.1736 | lr 0.0010 | time_forward 4.0260 | time_backward 6.0620 |
[2023-09-01 23:14:21,405::train::INFO] [train] Iter 03390 | loss 2.4882 | loss(rot) 2.0401 | loss(pos) 0.3197 | loss(seq) 0.1284 | grad 4.7420 | lr 0.0010 | time_forward 3.5780 | time_backward 4.8530 |
[2023-09-01 23:14:30,716::train::INFO] [train] Iter 03391 | loss 1.1423 | loss(rot) 0.4541 | loss(pos) 0.2626 | loss(seq) 0.4256 | grad 3.3399 | lr 0.0010 | time_forward 3.9270 | time_backward 5.3800 |
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