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[2023-10-23 15:43:21,731::train::INFO] [train] Iter 572580 | loss 1.6373 | loss(rot) 0.8911 | loss(pos) 0.3058 | loss(seq) 0.4404 | grad 4.5054 | lr 0.0000 | time_forward 1.1920 | time_backward 1.2460 |
[2023-10-23 15:43:24,389::train::INFO] [train] Iter 572581 | loss 0.8895 | loss(rot) 0.0656 | loss(pos) 0.8166 | loss(seq) 0.0073 | grad 6.8840 | lr 0.0000 | time_forward 1.2690 | time_backward 1.3840 |
[2023-10-23 15:43:30,939::train::INFO] [train] Iter 572582 | loss 0.2962 | loss(rot) 0.1438 | loss(pos) 0.0597 | loss(seq) 0.0927 | grad 3.2267 | lr 0.0000 | time_forward 2.8120 | time_backward 3.7180 |
[2023-10-23 15:43:38,147::train::INFO] [train] Iter 572583 | loss 0.8735 | loss(rot) 0.5663 | loss(pos) 0.0304 | loss(seq) 0.2768 | grad 3.7260 | lr 0.0000 | time_forward 3.1670 | time_backward 4.0390 |
[2023-10-23 15:43:44,979::train::INFO] [train] Iter 572584 | loss 0.2084 | loss(rot) 0.1768 | loss(pos) 0.0314 | loss(seq) 0.0001 | grad 3.7107 | lr 0.0000 | time_forward 2.9610 | time_backward 3.8670 |
[2023-10-23 15:43:47,645::train::INFO] [train] Iter 572585 | loss 0.4004 | loss(rot) 0.0866 | loss(pos) 0.2772 | loss(seq) 0.0366 | grad 5.0371 | lr 0.0000 | time_forward 1.2630 | time_backward 1.4010 |
[2023-10-23 15:43:50,714::train::INFO] [train] Iter 572586 | loss 1.0012 | loss(rot) 0.4702 | loss(pos) 0.2188 | loss(seq) 0.3122 | grad 3.3309 | lr 0.0000 | time_forward 1.3940 | time_backward 1.6620 |
[2023-10-23 15:43:57,872::train::INFO] [train] Iter 572587 | loss 0.2779 | loss(rot) 0.1500 | loss(pos) 0.0182 | loss(seq) 0.1098 | grad 2.2928 | lr 0.0000 | time_forward 3.0830 | time_backward 4.0510 |
[2023-10-23 15:44:05,793::train::INFO] [train] Iter 572588 | loss 0.8202 | loss(rot) 0.1176 | loss(pos) 0.6967 | loss(seq) 0.0059 | grad 4.6818 | lr 0.0000 | time_forward 3.2380 | time_backward 4.6800 |
[2023-10-23 15:44:08,441::train::INFO] [train] Iter 572589 | loss 0.1005 | loss(rot) 0.0721 | loss(pos) 0.0251 | loss(seq) 0.0032 | grad 1.6986 | lr 0.0000 | time_forward 1.2620 | time_backward 1.3830 |
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