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[2023-10-24 15:47:38,647::train::INFO] [train] Iter 584868 | loss 1.1910 | loss(rot) 0.6655 | loss(pos) 0.1587 | loss(seq) 0.3668 | grad 3.7652 | lr 0.0000 | time_forward 1.2500 | time_backward 1.2360 |
[2023-10-24 15:47:45,885::train::INFO] [train] Iter 584869 | loss 0.5621 | loss(rot) 0.5080 | loss(pos) 0.0223 | loss(seq) 0.0318 | grad 3.3096 | lr 0.0000 | time_forward 2.8790 | time_backward 4.3550 |
[2023-10-24 15:47:55,191::train::INFO] [train] Iter 584870 | loss 0.4529 | loss(rot) 0.1918 | loss(pos) 0.0872 | loss(seq) 0.1739 | grad 3.0807 | lr 0.0000 | time_forward 3.9260 | time_backward 5.3760 |
[2023-10-24 15:47:58,257::train::INFO] [train] Iter 584871 | loss 0.9127 | loss(rot) 0.4315 | loss(pos) 0.4793 | loss(seq) 0.0020 | grad 7.7488 | lr 0.0000 | time_forward 1.3810 | time_backward 1.6810 |
[2023-10-24 15:48:01,293::train::INFO] [train] Iter 584872 | loss 0.4153 | loss(rot) 0.1114 | loss(pos) 0.2260 | loss(seq) 0.0779 | grad 2.8414 | lr 0.0000 | time_forward 1.4830 | time_backward 1.5490 |
[2023-10-24 15:48:10,049::train::INFO] [train] Iter 584873 | loss 0.5078 | loss(rot) 0.0699 | loss(pos) 0.2543 | loss(seq) 0.1835 | grad 5.3393 | lr 0.0000 | time_forward 3.6290 | time_backward 5.1240 |
[2023-10-24 15:48:18,692::train::INFO] [train] Iter 584874 | loss 0.4498 | loss(rot) 0.1139 | loss(pos) 0.0866 | loss(seq) 0.2494 | grad 3.1037 | lr 0.0000 | time_forward 3.4260 | time_backward 5.2130 |
[2023-10-24 15:48:26,160::train::INFO] [train] Iter 584875 | loss 0.1295 | loss(rot) 0.1081 | loss(pos) 0.0202 | loss(seq) 0.0012 | grad 1.9315 | lr 0.0000 | time_forward 3.2680 | time_backward 4.1970 |
[2023-10-24 15:48:28,912::train::INFO] [train] Iter 584876 | loss 1.0993 | loss(rot) 0.8053 | loss(pos) 0.0239 | loss(seq) 0.2702 | grad 6.6511 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4210 |
[2023-10-24 15:48:37,503::train::INFO] [train] Iter 584877 | loss 0.6653 | loss(rot) 0.4678 | loss(pos) 0.0370 | loss(seq) 0.1604 | grad 29.1545 | lr 0.0000 | time_forward 3.5650 | time_backward 5.0130 |
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