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[2023-10-25 06:55:27,174::train::INFO] [train] Iter 592960 | loss 1.0312 | loss(rot) 0.2620 | loss(pos) 0.7334 | loss(seq) 0.0358 | grad 6.2186 | lr 0.0000 | time_forward 3.0350 | time_backward 4.0940 |
[2023-10-25 06:55:29,868::train::INFO] [train] Iter 592961 | loss 2.4241 | loss(rot) 0.6994 | loss(pos) 1.2091 | loss(seq) 0.5156 | grad 12.7981 | lr 0.0000 | time_forward 1.2850 | time_backward 1.4050 |
[2023-10-25 06:55:37,064::train::INFO] [train] Iter 592962 | loss 0.9289 | loss(rot) 0.5339 | loss(pos) 0.2275 | loss(seq) 0.1675 | grad 4.2332 | lr 0.0000 | time_forward 3.0570 | time_backward 4.1360 |
[2023-10-25 06:55:46,008::train::INFO] [train] Iter 592963 | loss 0.3097 | loss(rot) 0.1467 | loss(pos) 0.0178 | loss(seq) 0.1451 | grad 2.4555 | lr 0.0000 | time_forward 3.6800 | time_backward 5.2610 |
[2023-10-25 06:55:53,444::train::INFO] [train] Iter 592964 | loss 0.4774 | loss(rot) 0.1182 | loss(pos) 0.0927 | loss(seq) 0.2666 | grad 3.7141 | lr 0.0000 | time_forward 3.1960 | time_backward 4.2370 |
[2023-10-25 06:56:02,521::train::INFO] [train] Iter 592965 | loss 0.3781 | loss(rot) 0.1537 | loss(pos) 0.0368 | loss(seq) 0.1876 | grad 4.2867 | lr 0.0000 | time_forward 3.7210 | time_backward 5.3540 |
[2023-10-25 06:56:05,728::train::INFO] [train] Iter 592966 | loss 1.8278 | loss(rot) 1.7052 | loss(pos) 0.0315 | loss(seq) 0.0912 | grad 3.0864 | lr 0.0000 | time_forward 1.4470 | time_backward 1.7560 |
[2023-10-25 06:56:14,716::train::INFO] [train] Iter 592967 | loss 0.6714 | loss(rot) 0.6567 | loss(pos) 0.0130 | loss(seq) 0.0017 | grad 16.0720 | lr 0.0000 | time_forward 3.6900 | time_backward 5.2810 |
[2023-10-25 06:56:23,058::train::INFO] [train] Iter 592968 | loss 1.5104 | loss(rot) 1.2963 | loss(pos) 0.0740 | loss(seq) 0.1401 | grad 9.1034 | lr 0.0000 | time_forward 3.6180 | time_backward 4.7220 |
[2023-10-25 06:56:25,753::train::INFO] [train] Iter 592969 | loss 0.3690 | loss(rot) 0.0236 | loss(pos) 0.3427 | loss(seq) 0.0027 | grad 4.8223 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4150 |
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