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[2023-10-25 15:11:25,018::train::INFO] [train] Iter 597154 | loss 0.3747 | loss(rot) 0.0258 | loss(pos) 0.3396 | loss(seq) 0.0093 | grad 6.3358 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4250 |
[2023-10-25 15:11:33,188::train::INFO] [train] Iter 597155 | loss 1.5414 | loss(rot) 0.9120 | loss(pos) 0.0829 | loss(seq) 0.5465 | grad 3.9298 | lr 0.0000 | time_forward 3.4920 | time_backward 4.6760 |
[2023-10-25 15:11:42,121::train::INFO] [train] Iter 597156 | loss 1.1090 | loss(rot) 0.7711 | loss(pos) 0.0645 | loss(seq) 0.2734 | grad 6.0072 | lr 0.0000 | time_forward 3.8100 | time_backward 5.1190 |
[2023-10-25 15:11:50,530::train::INFO] [train] Iter 597157 | loss 1.3894 | loss(rot) 1.3612 | loss(pos) 0.0271 | loss(seq) 0.0010 | grad 3.7531 | lr 0.0000 | time_forward 3.6050 | time_backward 4.8000 |
[2023-10-25 15:11:57,996::train::INFO] [train] Iter 597158 | loss 0.3408 | loss(rot) 0.1488 | loss(pos) 0.0317 | loss(seq) 0.1604 | grad 3.5237 | lr 0.0000 | time_forward 3.2420 | time_backward 4.2200 |
[2023-10-25 15:12:05,291::train::INFO] [train] Iter 597159 | loss 2.2110 | loss(rot) 2.1794 | loss(pos) 0.0164 | loss(seq) 0.0151 | grad 3.9238 | lr 0.0000 | time_forward 3.1150 | time_backward 4.1770 |
[2023-10-25 15:12:13,801::train::INFO] [train] Iter 597160 | loss 1.0404 | loss(rot) 0.3517 | loss(pos) 0.3482 | loss(seq) 0.3404 | grad 2.7777 | lr 0.0000 | time_forward 3.5700 | time_backward 4.9370 |
[2023-10-25 15:12:21,275::train::INFO] [train] Iter 597161 | loss 2.5935 | loss(rot) 2.5603 | loss(pos) 0.0328 | loss(seq) 0.0004 | grad 4.6123 | lr 0.0000 | time_forward 3.1310 | time_backward 4.3400 |
[2023-10-25 15:12:29,659::train::INFO] [train] Iter 597162 | loss 0.2387 | loss(rot) 0.0258 | loss(pos) 0.1982 | loss(seq) 0.0147 | grad 2.9393 | lr 0.0000 | time_forward 3.5730 | time_backward 4.8080 |
[2023-10-25 15:12:39,109::train::INFO] [train] Iter 597163 | loss 1.5496 | loss(rot) 0.9391 | loss(pos) 0.1796 | loss(seq) 0.4309 | grad 4.0156 | lr 0.0000 | time_forward 3.8450 | time_backward 5.6010 |
[2023-10-25 15:12:48,634::train::INFO] [train] Iter 597164 | loss 1.1624 | loss(rot) 0.5502 | loss(pos) 0.0854 | loss(seq) 0.5268 | grad 3.6130 | lr 0.0000 | time_forward 4.0020 | time_backward 5.5190 |
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