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[2023-10-24 16:28:06,964::train::INFO] [train] Iter 585268 | loss 0.3914 | loss(rot) 0.3461 | loss(pos) 0.0293 | loss(seq) 0.0160 | grad 5.9998 | lr 0.0000 | time_forward 3.2900 | time_backward 4.5660 |
[2023-10-24 16:28:09,645::train::INFO] [train] Iter 585269 | loss 0.6881 | loss(rot) 0.0107 | loss(pos) 0.6765 | loss(seq) 0.0010 | grad 7.7392 | lr 0.0000 | time_forward 1.2650 | time_backward 1.4130 |
[2023-10-24 16:28:16,503::train::INFO] [train] Iter 585270 | loss 0.4328 | loss(rot) 0.1132 | loss(pos) 0.2732 | loss(seq) 0.0464 | grad 7.1630 | lr 0.0000 | time_forward 2.9650 | time_backward 3.8770 |
[2023-10-24 16:28:18,713::train::INFO] [train] Iter 585271 | loss 0.9451 | loss(rot) 0.1006 | loss(pos) 0.8437 | loss(seq) 0.0008 | grad 11.2896 | lr 0.0000 | time_forward 1.0090 | time_backward 1.1980 |
[2023-10-24 16:28:21,438::train::INFO] [train] Iter 585272 | loss 0.5390 | loss(rot) 0.1284 | loss(pos) 0.0530 | loss(seq) 0.3576 | grad 2.9991 | lr 0.0000 | time_forward 1.2830 | time_backward 1.4290 |
[2023-10-24 16:28:27,256::train::INFO] [train] Iter 585273 | loss 0.9750 | loss(rot) 0.9370 | loss(pos) 0.0372 | loss(seq) 0.0009 | grad 61.0267 | lr 0.0000 | time_forward 2.5710 | time_backward 3.2320 |
[2023-10-24 16:28:32,227::train::INFO] [train] Iter 585274 | loss 0.2388 | loss(rot) 0.1291 | loss(pos) 0.0826 | loss(seq) 0.0271 | grad 2.5404 | lr 0.0000 | time_forward 2.1690 | time_backward 2.8000 |
[2023-10-24 16:28:38,671::train::INFO] [train] Iter 585275 | loss 0.5925 | loss(rot) 0.2158 | loss(pos) 0.0396 | loss(seq) 0.3370 | grad 3.7850 | lr 0.0000 | time_forward 2.7450 | time_backward 3.6950 |
[2023-10-24 16:28:45,243::train::INFO] [train] Iter 585276 | loss 0.6010 | loss(rot) 0.2058 | loss(pos) 0.0372 | loss(seq) 0.3580 | grad 3.1042 | lr 0.0000 | time_forward 2.8190 | time_backward 3.7500 |
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