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[2023-10-23 19:52:12,562::train::INFO] [train] Iter 574778 | loss 0.5086 | loss(rot) 0.0880 | loss(pos) 0.4082 | loss(seq) 0.0125 | grad 9.4163 | lr 0.0000 | time_forward 3.3420 | time_backward 4.6460 |
[2023-10-23 19:52:15,398::train::INFO] [train] Iter 574779 | loss 0.9221 | loss(rot) 0.8848 | loss(pos) 0.0249 | loss(seq) 0.0124 | grad 16.5269 | lr 0.0000 | time_forward 1.3120 | time_backward 1.5210 |
[2023-10-23 19:52:17,779::train::INFO] [train] Iter 574780 | loss 0.7788 | loss(rot) 0.3299 | loss(pos) 0.1340 | loss(seq) 0.3149 | grad 2.7232 | lr 0.0000 | time_forward 1.0860 | time_backward 1.2930 |
[2023-10-23 19:52:20,566::train::INFO] [train] Iter 574781 | loss 0.9569 | loss(rot) 0.9011 | loss(pos) 0.0557 | loss(seq) 0.0000 | grad 10.9458 | lr 0.0000 | time_forward 1.2650 | time_backward 1.5160 |
[2023-10-23 19:52:23,346::train::INFO] [train] Iter 574782 | loss 0.2643 | loss(rot) 0.2377 | loss(pos) 0.0086 | loss(seq) 0.0179 | grad 2.8663 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4410 |
[2023-10-23 19:52:26,169::train::INFO] [train] Iter 574783 | loss 0.9905 | loss(rot) 0.0053 | loss(pos) 0.9848 | loss(seq) 0.0004 | grad 8.5066 | lr 0.0000 | time_forward 1.3820 | time_backward 1.4380 |
[2023-10-23 19:52:36,052::train::INFO] [train] Iter 574784 | loss 0.3464 | loss(rot) 0.1585 | loss(pos) 0.0973 | loss(seq) 0.0905 | grad 3.4483 | lr 0.0000 | time_forward 4.7110 | time_backward 5.1680 |
[2023-10-23 19:52:38,876::train::INFO] [train] Iter 574785 | loss 0.4767 | loss(rot) 0.0488 | loss(pos) 0.0647 | loss(seq) 0.3632 | grad 3.3449 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4710 |
[2023-10-23 19:52:49,283::train::INFO] [train] Iter 574786 | loss 0.9717 | loss(rot) 0.9232 | loss(pos) 0.0203 | loss(seq) 0.0282 | grad 49.5058 | lr 0.0000 | time_forward 4.4290 | time_backward 5.9740 |
[2023-10-23 19:52:59,365::train::INFO] [train] Iter 574787 | loss 1.2865 | loss(rot) 1.0754 | loss(pos) 0.0295 | loss(seq) 0.1815 | grad 4.3629 | lr 0.0000 | time_forward 4.1210 | time_backward 5.9570 |
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