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[2023-10-25 17:44:47,914::train::INFO] [train] Iter 598554 | loss 0.5448 | loss(rot) 0.2742 | loss(pos) 0.0432 | loss(seq) 0.2273 | grad 3.0520 | lr 0.0000 | time_forward 3.2070 | time_backward 4.3140 |
[2023-10-25 17:44:56,177::train::INFO] [train] Iter 598555 | loss 0.6505 | loss(rot) 0.1335 | loss(pos) 0.2497 | loss(seq) 0.2672 | grad 2.6016 | lr 0.0000 | time_forward 3.5190 | time_backward 4.7300 |
[2023-10-25 17:45:04,974::train::INFO] [train] Iter 598556 | loss 1.9604 | loss(rot) 0.0150 | loss(pos) 1.9450 | loss(seq) 0.0005 | grad 15.8133 | lr 0.0000 | time_forward 3.8170 | time_backward 4.9760 |
[2023-10-25 17:45:14,463::train::INFO] [train] Iter 598557 | loss 0.3228 | loss(rot) 0.0657 | loss(pos) 0.0477 | loss(seq) 0.2095 | grad 1.8259 | lr 0.0000 | time_forward 3.7110 | time_backward 5.7750 |
[2023-10-25 17:45:24,139::train::INFO] [train] Iter 598558 | loss 1.1486 | loss(rot) 1.0677 | loss(pos) 0.0302 | loss(seq) 0.0507 | grad 3.8408 | lr 0.0000 | time_forward 3.8190 | time_backward 5.8530 |
[2023-10-25 17:45:35,045::train::INFO] [train] Iter 598559 | loss 1.3184 | loss(rot) 0.9169 | loss(pos) 0.0648 | loss(seq) 0.3367 | grad 5.2116 | lr 0.0000 | time_forward 4.5240 | time_backward 6.3780 |
[2023-10-25 17:45:45,703::train::INFO] [train] Iter 598560 | loss 0.7967 | loss(rot) 0.7409 | loss(pos) 0.0474 | loss(seq) 0.0084 | grad 3.3666 | lr 0.0000 | time_forward 4.5610 | time_backward 6.0950 |
[2023-10-25 17:45:47,980::train::INFO] [train] Iter 598561 | loss 0.3696 | loss(rot) 0.0364 | loss(pos) 0.3293 | loss(seq) 0.0038 | grad 3.4319 | lr 0.0000 | time_forward 1.0440 | time_backward 1.2290 |
[2023-10-25 17:45:55,181::train::INFO] [train] Iter 598562 | loss 1.3183 | loss(rot) 0.6223 | loss(pos) 0.1580 | loss(seq) 0.5381 | grad 4.9360 | lr 0.0000 | time_forward 3.0360 | time_backward 4.1470 |
[2023-10-25 17:45:58,487::train::INFO] [train] Iter 598563 | loss 0.5927 | loss(rot) 0.4120 | loss(pos) 0.0683 | loss(seq) 0.1123 | grad 3.2488 | lr 0.0000 | time_forward 1.4710 | time_backward 1.8310 |
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