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[2023-10-23 13:12:51,043::train::INFO] [train] Iter 571181 | loss 0.4668 | loss(rot) 0.1150 | loss(pos) 0.0282 | loss(seq) 0.3236 | grad 3.0315 | lr 0.0000 | time_forward 1.2590 | time_backward 1.4290 |
[2023-10-23 13:12:58,498::train::INFO] [train] Iter 571182 | loss 0.4195 | loss(rot) 0.2256 | loss(pos) 0.0364 | loss(seq) 0.1575 | grad 3.0352 | lr 0.0000 | time_forward 3.2180 | time_backward 4.2090 |
[2023-10-23 13:13:05,729::train::INFO] [train] Iter 571183 | loss 1.0528 | loss(rot) 0.5637 | loss(pos) 0.0770 | loss(seq) 0.4121 | grad 4.0212 | lr 0.0000 | time_forward 2.8470 | time_backward 4.3810 |
[2023-10-23 13:13:12,980::train::INFO] [train] Iter 571184 | loss 1.2665 | loss(rot) 0.9099 | loss(pos) 0.0915 | loss(seq) 0.2651 | grad 3.3106 | lr 0.0000 | time_forward 3.0940 | time_backward 4.1540 |
[2023-10-23 13:13:20,765::train::INFO] [train] Iter 571185 | loss 0.7113 | loss(rot) 0.6274 | loss(pos) 0.0191 | loss(seq) 0.0648 | grad 8.0722 | lr 0.0000 | time_forward 3.4190 | time_backward 4.3620 |
[2023-10-23 13:13:23,566::train::INFO] [train] Iter 571186 | loss 0.2012 | loss(rot) 0.0740 | loss(pos) 0.0258 | loss(seq) 0.1014 | grad 2.5418 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4850 |
[2023-10-23 13:13:31,329::train::INFO] [train] Iter 571187 | loss 1.4860 | loss(rot) 0.6727 | loss(pos) 0.4933 | loss(seq) 0.3200 | grad 4.9067 | lr 0.0000 | time_forward 3.3330 | time_backward 4.4100 |
[2023-10-23 13:13:39,072::train::INFO] [train] Iter 571188 | loss 0.3940 | loss(rot) 0.0709 | loss(pos) 0.0585 | loss(seq) 0.2646 | grad 3.3336 | lr 0.0000 | time_forward 3.3400 | time_backward 4.4000 |
[2023-10-23 13:13:47,101::train::INFO] [train] Iter 571189 | loss 0.8197 | loss(rot) 0.5398 | loss(pos) 0.0347 | loss(seq) 0.2453 | grad 4.3280 | lr 0.0000 | time_forward 3.3820 | time_backward 4.6440 |
[2023-10-23 13:13:54,099::train::INFO] [train] Iter 571190 | loss 0.3313 | loss(rot) 0.1276 | loss(pos) 0.0592 | loss(seq) 0.1444 | grad 3.9994 | lr 0.0000 | time_forward 2.9870 | time_backward 4.0090 |
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