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[2023-10-24 17:19:12,837::train::INFO] [train] Iter 585767 | loss 0.1494 | loss(rot) 0.1198 | loss(pos) 0.0293 | loss(seq) 0.0003 | grad 2.4216 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4610 |
[2023-10-24 17:19:15,602::train::INFO] [train] Iter 585768 | loss 0.5188 | loss(rot) 0.0870 | loss(pos) 0.0317 | loss(seq) 0.4002 | grad 3.1381 | lr 0.0000 | time_forward 1.3460 | time_backward 1.4160 |
[2023-10-24 17:19:24,182::train::INFO] [train] Iter 585769 | loss 0.3536 | loss(rot) 0.1173 | loss(pos) 0.2026 | loss(seq) 0.0338 | grad 3.1145 | lr 0.0000 | time_forward 3.5630 | time_backward 5.0140 |
[2023-10-24 17:19:32,796::train::INFO] [train] Iter 585770 | loss 0.3976 | loss(rot) 0.0603 | loss(pos) 0.3304 | loss(seq) 0.0069 | grad 5.4472 | lr 0.0000 | time_forward 3.5470 | time_backward 5.0630 |
[2023-10-24 17:19:36,102::train::INFO] [train] Iter 585771 | loss 1.0296 | loss(rot) 0.1872 | loss(pos) 0.8364 | loss(seq) 0.0060 | grad 4.5036 | lr 0.0000 | time_forward 1.4620 | time_backward 1.8410 |
[2023-10-24 17:19:38,841::train::INFO] [train] Iter 585772 | loss 0.9238 | loss(rot) 0.4630 | loss(pos) 0.1171 | loss(seq) 0.3437 | grad 4.6229 | lr 0.0000 | time_forward 1.2880 | time_backward 1.4470 |
[2023-10-24 17:19:46,090::train::INFO] [train] Iter 585773 | loss 0.3177 | loss(rot) 0.2798 | loss(pos) 0.0135 | loss(seq) 0.0244 | grad 5.6122 | lr 0.0000 | time_forward 3.1160 | time_backward 4.1300 |
[2023-10-24 17:19:53,506::train::INFO] [train] Iter 585774 | loss 1.1166 | loss(rot) 0.7978 | loss(pos) 0.1341 | loss(seq) 0.1847 | grad 3.7290 | lr 0.0000 | time_forward 3.1860 | time_backward 4.2260 |
[2023-10-24 17:20:02,110::train::INFO] [train] Iter 585775 | loss 1.6250 | loss(rot) 1.5981 | loss(pos) 0.0213 | loss(seq) 0.0056 | grad 7.4366 | lr 0.0000 | time_forward 3.6660 | time_backward 4.9360 |
[2023-10-24 17:20:04,840::train::INFO] [train] Iter 585776 | loss 0.1675 | loss(rot) 0.0781 | loss(pos) 0.0296 | loss(seq) 0.0598 | grad 1.6855 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4130 |
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