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[2023-10-22 22:49:36,949::train::INFO] [train] Iter 562890 | loss 1.2217 | loss(rot) 0.7019 | loss(pos) 0.1072 | loss(seq) 0.4126 | grad 3.0256 | lr 0.0000 | time_forward 2.7030 | time_backward 3.6310 |
[2023-10-22 22:49:43,591::train::INFO] [train] Iter 562891 | loss 0.5227 | loss(rot) 0.1124 | loss(pos) 0.0124 | loss(seq) 0.3980 | grad 2.6148 | lr 0.0000 | time_forward 2.8790 | time_backward 3.7600 |
[2023-10-22 22:49:50,208::train::INFO] [train] Iter 562892 | loss 0.4530 | loss(rot) 0.1602 | loss(pos) 0.0449 | loss(seq) 0.2479 | grad 2.8513 | lr 0.0000 | time_forward 2.8760 | time_backward 3.7380 |
[2023-10-22 22:49:52,901::train::INFO] [train] Iter 562893 | loss 0.3732 | loss(rot) 0.1344 | loss(pos) 0.0238 | loss(seq) 0.2149 | grad 2.5648 | lr 0.0000 | time_forward 1.2830 | time_backward 1.4060 |
[2023-10-22 22:49:59,580::train::INFO] [train] Iter 562894 | loss 0.6354 | loss(rot) 0.5947 | loss(pos) 0.0207 | loss(seq) 0.0200 | grad 83.0746 | lr 0.0000 | time_forward 2.9260 | time_backward 3.7470 |
[2023-10-22 22:50:07,383::train::INFO] [train] Iter 562895 | loss 1.8400 | loss(rot) 1.0658 | loss(pos) 0.3138 | loss(seq) 0.4604 | grad 4.0718 | lr 0.0000 | time_forward 3.2260 | time_backward 4.5740 |
[2023-10-22 22:50:10,038::train::INFO] [train] Iter 562896 | loss 1.7438 | loss(rot) 0.9698 | loss(pos) 0.1849 | loss(seq) 0.5892 | grad 8.5566 | lr 0.0000 | time_forward 1.2730 | time_backward 1.3780 |
[2023-10-22 22:50:17,896::train::INFO] [train] Iter 562897 | loss 1.5391 | loss(rot) 0.9102 | loss(pos) 0.2338 | loss(seq) 0.3951 | grad 4.3617 | lr 0.0000 | time_forward 3.2760 | time_backward 4.5800 |
[2023-10-22 22:50:25,780::train::INFO] [train] Iter 562898 | loss 0.8288 | loss(rot) 0.5240 | loss(pos) 0.0191 | loss(seq) 0.2857 | grad 5.7607 | lr 0.0000 | time_forward 3.2500 | time_backward 4.6310 |
[2023-10-22 22:50:28,582::train::INFO] [train] Iter 562899 | loss 0.0876 | loss(rot) 0.0560 | loss(pos) 0.0315 | loss(seq) 0.0002 | grad 1.4565 | lr 0.0000 | time_forward 1.2800 | time_backward 1.5180 |
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