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[2023-10-23 18:06:40,032::train::INFO] [train] Iter 573879 | loss 0.4247 | loss(rot) 0.3470 | loss(pos) 0.0271 | loss(seq) 0.0506 | grad 3.1877 | lr 0.0000 | time_forward 3.6560 | time_backward 5.1740 |
[2023-10-23 18:06:48,957::train::INFO] [train] Iter 573880 | loss 0.6142 | loss(rot) 0.4702 | loss(pos) 0.0333 | loss(seq) 0.1107 | grad 3.3057 | lr 0.0000 | time_forward 4.1510 | time_backward 4.7720 |
[2023-10-23 18:06:57,211::train::INFO] [train] Iter 573881 | loss 0.8354 | loss(rot) 0.6825 | loss(pos) 0.0296 | loss(seq) 0.1233 | grad 9.0052 | lr 0.0000 | time_forward 3.5280 | time_backward 4.7230 |
[2023-10-23 18:07:00,038::train::INFO] [train] Iter 573882 | loss 1.7035 | loss(rot) 1.3255 | loss(pos) 0.2207 | loss(seq) 0.1573 | grad 6.9532 | lr 0.0000 | time_forward 1.3160 | time_backward 1.5080 |
[2023-10-23 18:07:08,857::train::INFO] [train] Iter 573883 | loss 0.3906 | loss(rot) 0.1557 | loss(pos) 0.0546 | loss(seq) 0.1803 | grad 2.8501 | lr 0.0000 | time_forward 3.5750 | time_backward 5.2400 |
[2023-10-23 18:07:16,419::train::INFO] [train] Iter 573884 | loss 0.7251 | loss(rot) 0.6684 | loss(pos) 0.0209 | loss(seq) 0.0359 | grad 29.3463 | lr 0.0000 | time_forward 3.2630 | time_backward 4.2950 |
[2023-10-23 18:07:24,802::train::INFO] [train] Iter 573885 | loss 0.1398 | loss(rot) 0.1128 | loss(pos) 0.0143 | loss(seq) 0.0126 | grad 1.4151 | lr 0.0000 | time_forward 3.6170 | time_backward 4.7620 |
[2023-10-23 18:07:34,333::train::INFO] [train] Iter 573886 | loss 1.3177 | loss(rot) 0.1934 | loss(pos) 1.1207 | loss(seq) 0.0036 | grad 5.6230 | lr 0.0000 | time_forward 4.1640 | time_backward 5.3640 |
[2023-10-23 18:07:42,579::train::INFO] [train] Iter 573887 | loss 1.2675 | loss(rot) 0.8016 | loss(pos) 0.0321 | loss(seq) 0.4338 | grad 4.5810 | lr 0.0000 | time_forward 3.5400 | time_backward 4.7030 |
[2023-10-23 18:07:50,577::train::INFO] [train] Iter 573888 | loss 0.2514 | loss(rot) 0.0916 | loss(pos) 0.0519 | loss(seq) 0.1079 | grad 2.2735 | lr 0.0000 | time_forward 3.3770 | time_backward 4.6170 |
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