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[2023-10-23 18:52:39,119::train::INFO] [train] Iter 574278 | loss 1.0004 | loss(rot) 0.7707 | loss(pos) 0.0713 | loss(seq) 0.1584 | grad 8.1328 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4830 |
[2023-10-23 18:52:47,703::train::INFO] [train] Iter 574279 | loss 0.1946 | loss(rot) 0.1388 | loss(pos) 0.0409 | loss(seq) 0.0150 | grad 5.5963 | lr 0.0000 | time_forward 3.6500 | time_backward 4.9320 |
[2023-10-23 18:52:56,254::train::INFO] [train] Iter 574280 | loss 0.2624 | loss(rot) 0.0181 | loss(pos) 0.2310 | loss(seq) 0.0133 | grad 3.4136 | lr 0.0000 | time_forward 3.6100 | time_backward 4.9370 |
[2023-10-23 18:52:58,986::train::INFO] [train] Iter 574281 | loss 0.5209 | loss(rot) 0.1566 | loss(pos) 0.0577 | loss(seq) 0.3066 | grad 2.5099 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4250 |
[2023-10-23 18:53:08,975::train::INFO] [train] Iter 574282 | loss 0.1729 | loss(rot) 0.0656 | loss(pos) 0.0879 | loss(seq) 0.0193 | grad 2.0060 | lr 0.0000 | time_forward 4.1150 | time_backward 5.8710 |
[2023-10-23 18:53:11,778::train::INFO] [train] Iter 574283 | loss 0.4146 | loss(rot) 0.0754 | loss(pos) 0.3210 | loss(seq) 0.0182 | grad 8.4581 | lr 0.0000 | time_forward 1.3470 | time_backward 1.4520 |
[2023-10-23 18:53:20,888::train::INFO] [train] Iter 574284 | loss 0.1953 | loss(rot) 0.1623 | loss(pos) 0.0271 | loss(seq) 0.0058 | grad 3.1360 | lr 0.0000 | time_forward 3.9330 | time_backward 5.1730 |
[2023-10-23 18:53:30,531::train::INFO] [train] Iter 574285 | loss 0.7219 | loss(rot) 0.3772 | loss(pos) 0.0723 | loss(seq) 0.2724 | grad 4.3554 | lr 0.0000 | time_forward 3.9610 | time_backward 5.6790 |
[2023-10-23 18:53:39,326::train::INFO] [train] Iter 574286 | loss 0.9625 | loss(rot) 0.7809 | loss(pos) 0.0132 | loss(seq) 0.1684 | grad 4.0593 | lr 0.0000 | time_forward 3.6000 | time_backward 5.1910 |
[2023-10-23 18:53:49,024::train::INFO] [train] Iter 574287 | loss 0.4843 | loss(rot) 0.4608 | loss(pos) 0.0232 | loss(seq) 0.0003 | grad 2.9196 | lr 0.0000 | time_forward 3.9820 | time_backward 5.7120 |
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