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[2023-10-23 20:37:12,699::train::INFO] [train] Iter 575177 | loss 1.5985 | loss(rot) 0.9010 | loss(pos) 0.6829 | loss(seq) 0.0147 | grad 17.9416 | lr 0.0000 | time_forward 3.6570 | time_backward 5.1480 |
[2023-10-23 20:37:24,439::train::INFO] [train] Iter 575178 | loss 0.5590 | loss(rot) 0.2544 | loss(pos) 0.0528 | loss(seq) 0.2519 | grad 3.2168 | lr 0.0000 | time_forward 6.4880 | time_backward 5.2490 |
[2023-10-23 20:37:32,070::train::INFO] [train] Iter 575179 | loss 1.7318 | loss(rot) 1.0990 | loss(pos) 0.3183 | loss(seq) 0.3145 | grad 6.5938 | lr 0.0000 | time_forward 3.5120 | time_backward 4.1160 |
[2023-10-23 20:37:42,094::train::INFO] [train] Iter 575180 | loss 0.5752 | loss(rot) 0.0844 | loss(pos) 0.2438 | loss(seq) 0.2470 | grad 3.3445 | lr 0.0000 | time_forward 4.3080 | time_backward 5.7130 |
[2023-10-23 20:37:51,312::train::INFO] [train] Iter 575181 | loss 0.6341 | loss(rot) 0.1384 | loss(pos) 0.2833 | loss(seq) 0.2124 | grad 3.8977 | lr 0.0000 | time_forward 3.7660 | time_backward 5.4480 |
[2023-10-23 20:38:00,591::train::INFO] [train] Iter 575182 | loss 0.7879 | loss(rot) 0.7328 | loss(pos) 0.0234 | loss(seq) 0.0317 | grad 4.0093 | lr 0.0000 | time_forward 3.8230 | time_backward 5.4530 |
[2023-10-23 20:38:09,979::train::INFO] [train] Iter 575183 | loss 1.0716 | loss(rot) 0.8911 | loss(pos) 0.0484 | loss(seq) 0.1321 | grad 3.1974 | lr 0.0000 | time_forward 3.8380 | time_backward 5.5480 |
[2023-10-23 20:38:19,462::train::INFO] [train] Iter 575184 | loss 0.2338 | loss(rot) 0.1927 | loss(pos) 0.0410 | loss(seq) 0.0000 | grad 2.8698 | lr 0.0000 | time_forward 3.8450 | time_backward 5.6350 |
[2023-10-23 20:38:29,023::train::INFO] [train] Iter 575185 | loss 0.4882 | loss(rot) 0.0606 | loss(pos) 0.3487 | loss(seq) 0.0789 | grad 4.5310 | lr 0.0000 | time_forward 3.9200 | time_backward 5.6370 |
[2023-10-23 20:38:38,534::train::INFO] [train] Iter 575186 | loss 0.9822 | loss(rot) 0.5830 | loss(pos) 0.0607 | loss(seq) 0.3386 | grad 3.8560 | lr 0.0000 | time_forward 3.8650 | time_backward 5.6440 |
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