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[2023-09-02 17:04:32,466::train::INFO] [train] Iter 12273 | loss 1.9269 | loss(rot) 0.8155 | loss(pos) 0.4674 | loss(seq) 0.6441 | grad 4.4015 | lr 0.0010 | time_forward 3.0860 | time_backward 4.0820 |
[2023-09-02 17:04:40,764::train::INFO] [train] Iter 12274 | loss 2.1906 | loss(rot) 1.8011 | loss(pos) 0.2329 | loss(seq) 0.1566 | grad 10.3429 | lr 0.0010 | time_forward 3.3950 | time_backward 4.9000 |
[2023-09-02 17:04:43,334::train::INFO] [train] Iter 12275 | loss 1.2541 | loss(rot) 0.5441 | loss(pos) 0.1789 | loss(seq) 0.5311 | grad 4.4929 | lr 0.0010 | time_forward 1.1830 | time_backward 1.3850 |
[2023-09-02 17:04:45,920::train::INFO] [train] Iter 12276 | loss 1.7366 | loss(rot) 1.2396 | loss(pos) 0.1296 | loss(seq) 0.3674 | grad 4.2559 | lr 0.0010 | time_forward 1.4320 | time_backward 1.1500 |
[2023-09-02 17:04:48,427::train::INFO] [train] Iter 12277 | loss 1.8461 | loss(rot) 1.4707 | loss(pos) 0.1120 | loss(seq) 0.2634 | grad 4.1277 | lr 0.0010 | time_forward 1.1500 | time_backward 1.3550 |
[2023-09-02 17:04:56,707::train::INFO] [train] Iter 12278 | loss 1.8380 | loss(rot) 1.6219 | loss(pos) 0.1717 | loss(seq) 0.0444 | grad 4.9590 | lr 0.0010 | time_forward 3.3420 | time_backward 4.9340 |
[2023-09-02 17:05:04,701::train::INFO] [train] Iter 12279 | loss 2.3133 | loss(rot) 1.3136 | loss(pos) 0.4970 | loss(seq) 0.5027 | grad 4.2762 | lr 0.0010 | time_forward 3.8280 | time_backward 4.1620 |
[2023-09-02 17:05:13,597::train::INFO] [train] Iter 12280 | loss 1.5289 | loss(rot) 0.8113 | loss(pos) 0.2207 | loss(seq) 0.4970 | grad 5.3280 | lr 0.0010 | time_forward 3.4120 | time_backward 3.6890 |
[2023-09-02 17:05:23,079::train::INFO] [train] Iter 12281 | loss 1.8857 | loss(rot) 1.1691 | loss(pos) 0.2198 | loss(seq) 0.4968 | grad 2.6802 | lr 0.0010 | time_forward 4.9880 | time_backward 4.4910 |
[2023-09-02 17:05:25,577::train::INFO] [train] Iter 12282 | loss 1.6356 | loss(rot) 1.0319 | loss(pos) 0.1274 | loss(seq) 0.4763 | grad 4.7397 | lr 0.0010 | time_forward 1.1380 | time_backward 1.3560 |
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