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[2023-10-23 17:22:04,765::train::INFO] [train] Iter 573479 | loss 0.1501 | loss(rot) 0.0878 | loss(pos) 0.0187 | loss(seq) 0.0436 | grad 1.8991 | lr 0.0000 | time_forward 4.1630 | time_backward 4.9700 |
[2023-10-23 17:22:12,812::train::INFO] [train] Iter 573480 | loss 0.1791 | loss(rot) 0.0637 | loss(pos) 0.0182 | loss(seq) 0.0972 | grad 1.8233 | lr 0.0000 | time_forward 3.4280 | time_backward 4.6150 |
[2023-10-23 17:22:15,057::train::INFO] [train] Iter 573481 | loss 0.1536 | loss(rot) 0.1169 | loss(pos) 0.0273 | loss(seq) 0.0094 | grad 2.1657 | lr 0.0000 | time_forward 1.0550 | time_backward 1.1880 |
[2023-10-23 17:22:22,804::train::INFO] [train] Iter 573482 | loss 0.9396 | loss(rot) 0.3325 | loss(pos) 0.1516 | loss(seq) 0.4554 | grad 4.4424 | lr 0.0000 | time_forward 3.3140 | time_backward 4.4260 |
[2023-10-23 17:22:30,379::train::INFO] [train] Iter 573483 | loss 2.0784 | loss(rot) 1.4034 | loss(pos) 0.2123 | loss(seq) 0.4628 | grad 4.4049 | lr 0.0000 | time_forward 3.2750 | time_backward 4.2970 |
[2023-10-23 17:22:38,389::train::INFO] [train] Iter 573484 | loss 0.2292 | loss(rot) 0.0234 | loss(pos) 0.2030 | loss(seq) 0.0027 | grad 4.9535 | lr 0.0000 | time_forward 3.4250 | time_backward 4.5810 |
[2023-10-23 17:22:41,064::train::INFO] [train] Iter 573485 | loss 0.5107 | loss(rot) 0.2020 | loss(pos) 0.0821 | loss(seq) 0.2267 | grad 3.8441 | lr 0.0000 | time_forward 1.2590 | time_backward 1.4130 |
[2023-10-23 17:22:51,023::train::INFO] [train] Iter 573486 | loss 1.0436 | loss(rot) 0.7002 | loss(pos) 0.0654 | loss(seq) 0.2780 | grad 4.0057 | lr 0.0000 | time_forward 4.3650 | time_backward 5.5590 |
[2023-10-23 17:22:59,966::train::INFO] [train] Iter 573487 | loss 0.5672 | loss(rot) 0.0728 | loss(pos) 0.4186 | loss(seq) 0.0757 | grad 6.3240 | lr 0.0000 | time_forward 3.6890 | time_backward 5.2500 |
[2023-10-23 17:23:09,835::train::INFO] [train] Iter 573488 | loss 0.3885 | loss(rot) 0.1307 | loss(pos) 0.0486 | loss(seq) 0.2092 | grad 2.0294 | lr 0.0000 | time_forward 4.5310 | time_backward 5.3340 |
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