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[2023-10-23 01:13:38,670::train::INFO] [train] Iter 564289 | loss 0.3991 | loss(rot) 0.0055 | loss(pos) 0.3902 | loss(seq) 0.0034 | grad 11.1446 | lr 0.0000 | time_forward 2.6630 | time_backward 3.4460 |
[2023-10-23 01:13:46,656::train::INFO] [train] Iter 564290 | loss 0.8557 | loss(rot) 0.7350 | loss(pos) 0.0367 | loss(seq) 0.0840 | grad 3.3075 | lr 0.0000 | time_forward 3.3540 | time_backward 4.6300 |
[2023-10-23 01:13:54,676::train::INFO] [train] Iter 564291 | loss 0.4986 | loss(rot) 0.0562 | loss(pos) 0.0896 | loss(seq) 0.3528 | grad 3.4933 | lr 0.0000 | time_forward 3.3260 | time_backward 4.6910 |
[2023-10-23 01:13:59,843::train::INFO] [train] Iter 564292 | loss 0.9342 | loss(rot) 0.6675 | loss(pos) 0.0280 | loss(seq) 0.2388 | grad 9.3491 | lr 0.0000 | time_forward 2.3180 | time_backward 2.8460 |
[2023-10-23 01:14:08,109::train::INFO] [train] Iter 564293 | loss 0.2544 | loss(rot) 0.1729 | loss(pos) 0.0814 | loss(seq) 0.0000 | grad 8.5028 | lr 0.0000 | time_forward 3.4560 | time_backward 4.8060 |
[2023-10-23 01:14:10,769::train::INFO] [train] Iter 564294 | loss 2.8269 | loss(rot) 0.0224 | loss(pos) 2.8042 | loss(seq) 0.0003 | grad 22.6061 | lr 0.0000 | time_forward 1.2570 | time_backward 1.3990 |
[2023-10-23 01:14:19,348::train::INFO] [train] Iter 564295 | loss 0.4277 | loss(rot) 0.1317 | loss(pos) 0.2485 | loss(seq) 0.0474 | grad 3.6198 | lr 0.0000 | time_forward 3.5700 | time_backward 4.9920 |
[2023-10-23 01:14:24,514::train::INFO] [train] Iter 564296 | loss 0.9090 | loss(rot) 0.8876 | loss(pos) 0.0196 | loss(seq) 0.0018 | grad 5.6954 | lr 0.0000 | time_forward 2.3110 | time_backward 2.8530 |
[2023-10-23 01:14:31,186::train::INFO] [train] Iter 564297 | loss 1.3944 | loss(rot) 0.7711 | loss(pos) 0.1157 | loss(seq) 0.5076 | grad 3.7288 | lr 0.0000 | time_forward 2.9980 | time_backward 3.6700 |
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