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[2023-10-24 20:17:12,249::train::INFO] [train] Iter 587366 | loss 0.3908 | loss(rot) 0.2090 | loss(pos) 0.0212 | loss(seq) 0.1606 | grad 2.8546 | lr 0.0000 | time_forward 3.0760 | time_backward 4.0880 |
[2023-10-24 20:17:19,699::train::INFO] [train] Iter 587367 | loss 0.1453 | loss(rot) 0.0309 | loss(pos) 0.0265 | loss(seq) 0.0879 | grad 1.9273 | lr 0.0000 | time_forward 3.1670 | time_backward 4.2800 |
[2023-10-24 20:17:27,999::train::INFO] [train] Iter 587368 | loss 1.2186 | loss(rot) 0.8652 | loss(pos) 0.0716 | loss(seq) 0.2817 | grad 9.2683 | lr 0.0000 | time_forward 3.5800 | time_backward 4.7170 |
[2023-10-24 20:17:35,581::train::INFO] [train] Iter 587369 | loss 0.7276 | loss(rot) 0.2313 | loss(pos) 0.0475 | loss(seq) 0.4488 | grad 3.5922 | lr 0.0000 | time_forward 3.2220 | time_backward 4.3560 |
[2023-10-24 20:17:44,593::train::INFO] [train] Iter 587370 | loss 0.2488 | loss(rot) 0.0687 | loss(pos) 0.1630 | loss(seq) 0.0171 | grad 3.0766 | lr 0.0000 | time_forward 3.6990 | time_backward 5.3100 |
[2023-10-24 20:17:53,626::train::INFO] [train] Iter 587371 | loss 0.9749 | loss(rot) 0.8922 | loss(pos) 0.0826 | loss(seq) 0.0000 | grad 6.9470 | lr 0.0000 | time_forward 3.6960 | time_backward 5.3340 |
[2023-10-24 20:18:02,634::train::INFO] [train] Iter 587372 | loss 1.5076 | loss(rot) 0.9981 | loss(pos) 0.1340 | loss(seq) 0.3755 | grad 4.1154 | lr 0.0000 | time_forward 3.6830 | time_backward 5.3220 |
[2023-10-24 20:18:12,209::train::INFO] [train] Iter 587373 | loss 1.7081 | loss(rot) 1.5577 | loss(pos) 0.0894 | loss(seq) 0.0609 | grad 3.3040 | lr 0.0000 | time_forward 4.0520 | time_backward 5.5200 |
[2023-10-24 20:18:18,039::train::INFO] [train] Iter 587374 | loss 0.2942 | loss(rot) 0.0985 | loss(pos) 0.0377 | loss(seq) 0.1580 | grad 2.4874 | lr 0.0000 | time_forward 2.4990 | time_backward 3.3270 |
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