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[2023-10-24 14:18:45,094::train::INFO] [train] Iter 584068 | loss 2.0749 | loss(rot) 0.3791 | loss(pos) 1.6952 | loss(seq) 0.0006 | grad 9.2718 | lr 0.0000 | time_forward 3.9900 | time_backward 5.8040 |
[2023-10-24 14:18:55,015::train::INFO] [train] Iter 584069 | loss 1.2842 | loss(rot) 0.6939 | loss(pos) 0.2678 | loss(seq) 0.3224 | grad 4.6131 | lr 0.0000 | time_forward 4.1410 | time_backward 5.7770 |
[2023-10-24 14:19:04,871::train::INFO] [train] Iter 584070 | loss 0.6301 | loss(rot) 0.5434 | loss(pos) 0.0377 | loss(seq) 0.0490 | grad 6.1827 | lr 0.0000 | time_forward 3.9850 | time_backward 5.8680 |
[2023-10-24 14:19:08,180::train::INFO] [train] Iter 584071 | loss 0.8992 | loss(rot) 0.7855 | loss(pos) 0.0334 | loss(seq) 0.0804 | grad 9.9780 | lr 0.0000 | time_forward 1.4980 | time_backward 1.8080 |
[2023-10-24 14:19:16,578::train::INFO] [train] Iter 584072 | loss 0.5284 | loss(rot) 0.0594 | loss(pos) 0.4658 | loss(seq) 0.0032 | grad 6.7707 | lr 0.0000 | time_forward 3.4380 | time_backward 4.9420 |
[2023-10-24 14:19:19,495::train::INFO] [train] Iter 584073 | loss 0.6826 | loss(rot) 0.0820 | loss(pos) 0.0928 | loss(seq) 0.5078 | grad 2.7508 | lr 0.0000 | time_forward 1.3690 | time_backward 1.5450 |
[2023-10-24 14:19:22,473::train::INFO] [train] Iter 584074 | loss 0.8560 | loss(rot) 0.5075 | loss(pos) 0.0304 | loss(seq) 0.3182 | grad 5.0104 | lr 0.0000 | time_forward 1.3910 | time_backward 1.5490 |
[2023-10-24 14:19:31,250::train::INFO] [train] Iter 584075 | loss 0.6986 | loss(rot) 0.2392 | loss(pos) 0.0405 | loss(seq) 0.4189 | grad 4.1785 | lr 0.0000 | time_forward 3.7410 | time_backward 5.0340 |
[2023-10-24 14:19:39,827::train::INFO] [train] Iter 584076 | loss 0.7479 | loss(rot) 0.1418 | loss(pos) 0.0799 | loss(seq) 0.5261 | grad 4.0668 | lr 0.0000 | time_forward 3.5820 | time_backward 4.9910 |
[2023-10-24 14:19:48,867::train::INFO] [train] Iter 584077 | loss 1.2294 | loss(rot) 0.6520 | loss(pos) 0.0845 | loss(seq) 0.4929 | grad 5.1025 | lr 0.0000 | time_forward 3.8180 | time_backward 5.2200 |
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