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[2023-10-24 02:09:51,345::train::INFO] [train] Iter 577975 | loss 1.0963 | loss(rot) 0.4165 | loss(pos) 0.4104 | loss(seq) 0.2695 | grad 7.3470 | lr 0.0000 | time_forward 3.9850 | time_backward 5.7030 |
[2023-10-24 02:10:00,998::train::INFO] [train] Iter 577976 | loss 0.2695 | loss(rot) 0.2145 | loss(pos) 0.0148 | loss(seq) 0.0402 | grad 3.4820 | lr 0.0000 | time_forward 3.9190 | time_backward 5.7310 |
[2023-10-24 02:10:09,125::train::INFO] [train] Iter 577977 | loss 0.2687 | loss(rot) 0.0328 | loss(pos) 0.2300 | loss(seq) 0.0060 | grad 5.4777 | lr 0.0000 | time_forward 3.4400 | time_backward 4.6850 |
[2023-10-24 02:10:18,642::train::INFO] [train] Iter 577978 | loss 0.9034 | loss(rot) 0.6529 | loss(pos) 0.0584 | loss(seq) 0.1922 | grad 3.0719 | lr 0.0000 | time_forward 3.8780 | time_backward 5.6360 |
[2023-10-24 02:10:27,476::train::INFO] [train] Iter 577979 | loss 0.2570 | loss(rot) 0.2247 | loss(pos) 0.0186 | loss(seq) 0.0136 | grad 3.3568 | lr 0.0000 | time_forward 3.6820 | time_backward 5.1480 |
[2023-10-24 02:10:30,179::train::INFO] [train] Iter 577980 | loss 0.5351 | loss(rot) 0.2768 | loss(pos) 0.0283 | loss(seq) 0.2301 | grad 2.3579 | lr 0.0000 | time_forward 1.2650 | time_backward 1.4350 |
[2023-10-24 02:10:33,012::train::INFO] [train] Iter 577981 | loss 1.4879 | loss(rot) 0.8994 | loss(pos) 0.1221 | loss(seq) 0.4664 | grad 5.7013 | lr 0.0000 | time_forward 1.3680 | time_backward 1.4610 |
[2023-10-24 02:10:35,841::train::INFO] [train] Iter 577982 | loss 0.3777 | loss(rot) 0.1397 | loss(pos) 0.0262 | loss(seq) 0.2118 | grad 1.8707 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4680 |
[2023-10-24 02:10:44,073::train::INFO] [train] Iter 577983 | loss 0.2579 | loss(rot) 0.1489 | loss(pos) 0.0435 | loss(seq) 0.0656 | grad 2.2808 | lr 0.0000 | time_forward 3.4980 | time_backward 4.7300 |
[2023-10-24 02:10:52,267::train::INFO] [train] Iter 577984 | loss 0.9102 | loss(rot) 0.8134 | loss(pos) 0.0290 | loss(seq) 0.0679 | grad 25.3838 | lr 0.0000 | time_forward 3.4780 | time_backward 4.7140 |
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