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[2023-10-23 22:36:03,268::train::INFO] [train] Iter 576176 | loss 0.9419 | loss(rot) 0.9046 | loss(pos) 0.0353 | loss(seq) 0.0021 | grad 4.0673 | lr 0.0000 | time_forward 4.3350 | time_backward 5.7710 |
[2023-10-23 22:36:12,353::train::INFO] [train] Iter 576177 | loss 0.2497 | loss(rot) 0.1998 | loss(pos) 0.0196 | loss(seq) 0.0303 | grad 5.0698 | lr 0.0000 | time_forward 3.7920 | time_backward 5.2890 |
[2023-10-23 22:36:21,618::train::INFO] [train] Iter 576178 | loss 0.5569 | loss(rot) 0.0079 | loss(pos) 0.5487 | loss(seq) 0.0003 | grad 9.3170 | lr 0.0000 | time_forward 3.9710 | time_backward 5.2920 |
[2023-10-23 22:36:29,449::train::INFO] [train] Iter 576179 | loss 0.3675 | loss(rot) 0.2869 | loss(pos) 0.0801 | loss(seq) 0.0005 | grad 3.4461 | lr 0.0000 | time_forward 3.2970 | time_backward 4.5300 |
[2023-10-23 22:36:39,279::train::INFO] [train] Iter 576180 | loss 0.6782 | loss(rot) 0.3087 | loss(pos) 0.0972 | loss(seq) 0.2724 | grad 3.1816 | lr 0.0000 | time_forward 4.1690 | time_backward 5.6580 |
[2023-10-23 22:36:42,017::train::INFO] [train] Iter 576181 | loss 0.1708 | loss(rot) 0.0286 | loss(pos) 0.0243 | loss(seq) 0.1179 | grad 1.4907 | lr 0.0000 | time_forward 1.3020 | time_backward 1.4340 |
[2023-10-23 22:36:50,790::train::INFO] [train] Iter 576182 | loss 0.3173 | loss(rot) 0.1381 | loss(pos) 0.0426 | loss(seq) 0.1366 | grad 2.2641 | lr 0.0000 | time_forward 3.7560 | time_backward 5.0140 |
[2023-10-23 22:36:58,721::train::INFO] [train] Iter 576183 | loss 0.6596 | loss(rot) 0.2657 | loss(pos) 0.0440 | loss(seq) 0.3499 | grad 3.8085 | lr 0.0000 | time_forward 3.3510 | time_backward 4.5760 |
[2023-10-23 22:37:07,067::train::INFO] [train] Iter 576184 | loss 0.4674 | loss(rot) 0.2049 | loss(pos) 0.1537 | loss(seq) 0.1087 | grad 4.6088 | lr 0.0000 | time_forward 3.3420 | time_backward 5.0020 |
[2023-10-23 22:37:30,297::train::INFO] [train] Iter 576185 | loss 0.6927 | loss(rot) 0.3052 | loss(pos) 0.0414 | loss(seq) 0.3461 | grad 3.0371 | lr 0.0000 | time_forward 11.9960 | time_backward 11.2310 |
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