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[2023-10-24 00:57:53,347::train::INFO] [train] Iter 577375 | loss 0.6714 | loss(rot) 0.2182 | loss(pos) 0.1637 | loss(seq) 0.2895 | grad 3.3129 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4410 |
[2023-10-24 00:57:55,956::train::INFO] [train] Iter 577376 | loss 0.7458 | loss(rot) 0.2541 | loss(pos) 0.0168 | loss(seq) 0.4749 | grad 26.8317 | lr 0.0000 | time_forward 1.2730 | time_backward 1.3330 |
[2023-10-24 00:58:03,924::train::INFO] [train] Iter 577377 | loss 0.6914 | loss(rot) 0.3592 | loss(pos) 0.0666 | loss(seq) 0.2655 | grad 3.3236 | lr 0.0000 | time_forward 3.3270 | time_backward 4.6370 |
[2023-10-24 00:58:12,635::train::INFO] [train] Iter 577378 | loss 2.7342 | loss(rot) 0.1164 | loss(pos) 2.6179 | loss(seq) 0.0000 | grad 17.1387 | lr 0.0000 | time_forward 3.6020 | time_backward 5.1060 |
[2023-10-24 00:58:21,072::train::INFO] [train] Iter 577379 | loss 1.2290 | loss(rot) 0.9790 | loss(pos) 0.0870 | loss(seq) 0.1630 | grad 7.7989 | lr 0.0000 | time_forward 3.5740 | time_backward 4.8590 |
[2023-10-24 00:58:23,867::train::INFO] [train] Iter 577380 | loss 0.2853 | loss(rot) 0.2282 | loss(pos) 0.0406 | loss(seq) 0.0165 | grad 4.4476 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4570 |
[2023-10-24 00:58:32,515::train::INFO] [train] Iter 577381 | loss 0.1776 | loss(rot) 0.1065 | loss(pos) 0.0290 | loss(seq) 0.0422 | grad 2.9970 | lr 0.0000 | time_forward 3.6710 | time_backward 4.9740 |
[2023-10-24 00:58:35,235::train::INFO] [train] Iter 577382 | loss 0.2945 | loss(rot) 0.0477 | loss(pos) 0.2352 | loss(seq) 0.0116 | grad 5.1996 | lr 0.0000 | time_forward 1.2740 | time_backward 1.4430 |
[2023-10-24 00:58:38,056::train::INFO] [train] Iter 577383 | loss 1.4427 | loss(rot) 0.8374 | loss(pos) 0.1130 | loss(seq) 0.4923 | grad 4.9067 | lr 0.0000 | time_forward 1.3200 | time_backward 1.4680 |
[2023-10-24 00:58:40,941::train::INFO] [train] Iter 577384 | loss 0.4111 | loss(rot) 0.0548 | loss(pos) 0.0557 | loss(seq) 0.3006 | grad 3.2682 | lr 0.0000 | time_forward 1.3690 | time_backward 1.5120 |
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