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[2023-10-23 02:52:26,357::train::INFO] [train] Iter 565288 | loss 0.2898 | loss(rot) 0.2760 | loss(pos) 0.0114 | loss(seq) 0.0024 | grad 3.7323 | lr 0.0000 | time_forward 2.8970 | time_backward 3.7560 |
[2023-10-23 02:52:33,435::train::INFO] [train] Iter 565289 | loss 0.1240 | loss(rot) 0.0623 | loss(pos) 0.0092 | loss(seq) 0.0524 | grad 2.0765 | lr 0.0000 | time_forward 2.9880 | time_backward 4.0860 |
[2023-10-23 02:52:41,446::train::INFO] [train] Iter 565290 | loss 0.8736 | loss(rot) 0.0139 | loss(pos) 0.8587 | loss(seq) 0.0010 | grad 8.7741 | lr 0.0000 | time_forward 3.3170 | time_backward 4.6920 |
[2023-10-23 02:52:44,019::train::INFO] [train] Iter 565291 | loss 0.8880 | loss(rot) 0.8487 | loss(pos) 0.0383 | loss(seq) 0.0010 | grad 6.0014 | lr 0.0000 | time_forward 1.1870 | time_backward 1.3820 |
[2023-10-23 02:52:51,027::train::INFO] [train] Iter 565292 | loss 0.8059 | loss(rot) 0.5126 | loss(pos) 0.0499 | loss(seq) 0.2434 | grad 4.5309 | lr 0.0000 | time_forward 3.1310 | time_backward 3.8630 |
[2023-10-23 02:52:58,878::train::INFO] [train] Iter 565293 | loss 0.3439 | loss(rot) 0.0329 | loss(pos) 0.0203 | loss(seq) 0.2906 | grad 2.6130 | lr 0.0000 | time_forward 3.2670 | time_backward 4.5820 |
[2023-10-23 02:53:06,844::train::INFO] [train] Iter 565294 | loss 0.1806 | loss(rot) 0.1574 | loss(pos) 0.0232 | loss(seq) 0.0000 | grad 2.8704 | lr 0.0000 | time_forward 3.3040 | time_backward 4.6580 |
[2023-10-23 02:53:14,098::train::INFO] [train] Iter 565295 | loss 0.3977 | loss(rot) 0.3636 | loss(pos) 0.0336 | loss(seq) 0.0005 | grad 2.9219 | lr 0.0000 | time_forward 3.2030 | time_backward 4.0480 |
[2023-10-23 02:53:16,326::train::INFO] [train] Iter 565296 | loss 0.4897 | loss(rot) 0.1625 | loss(pos) 0.1760 | loss(seq) 0.1512 | grad 2.8136 | lr 0.0000 | time_forward 1.0100 | time_backward 1.2140 |
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