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[2023-10-23 02:12:28,114::train::INFO] [train] Iter 564888 | loss 0.6187 | loss(rot) 0.2564 | loss(pos) 0.0858 | loss(seq) 0.2765 | grad 3.1110 | lr 0.0000 | time_forward 1.2570 | time_backward 1.4080 |
[2023-10-23 02:12:31,219::train::INFO] [train] Iter 564889 | loss 1.0078 | loss(rot) 0.1171 | loss(pos) 0.8634 | loss(seq) 0.0273 | grad 5.3794 | lr 0.0000 | time_forward 1.4200 | time_backward 1.6810 |
[2023-10-23 02:12:37,736::train::INFO] [train] Iter 564890 | loss 1.6460 | loss(rot) 0.0989 | loss(pos) 1.5422 | loss(seq) 0.0050 | grad 10.9705 | lr 0.0000 | time_forward 2.7400 | time_backward 3.7720 |
[2023-10-23 02:12:44,864::train::INFO] [train] Iter 564891 | loss 0.5098 | loss(rot) 0.0686 | loss(pos) 0.1251 | loss(seq) 0.3161 | grad 3.6839 | lr 0.0000 | time_forward 3.0910 | time_backward 4.0210 |
[2023-10-23 02:12:52,995::train::INFO] [train] Iter 564892 | loss 0.4298 | loss(rot) 0.0705 | loss(pos) 0.0637 | loss(seq) 0.2956 | grad 2.9309 | lr 0.0000 | time_forward 3.4910 | time_backward 4.6380 |
[2023-10-23 02:12:55,658::train::INFO] [train] Iter 564893 | loss 1.2072 | loss(rot) 1.1796 | loss(pos) 0.0162 | loss(seq) 0.0115 | grad 5.3735 | lr 0.0000 | time_forward 1.2730 | time_backward 1.3860 |
[2023-10-23 02:13:02,324::train::INFO] [train] Iter 564894 | loss 0.1495 | loss(rot) 0.1072 | loss(pos) 0.0244 | loss(seq) 0.0178 | grad 1.8758 | lr 0.0000 | time_forward 2.9020 | time_backward 3.7600 |
[2023-10-23 02:13:04,982::train::INFO] [train] Iter 564895 | loss 0.4377 | loss(rot) 0.0781 | loss(pos) 0.0466 | loss(seq) 0.3130 | grad 2.6942 | lr 0.0000 | time_forward 1.2830 | time_backward 1.3720 |
[2023-10-23 02:13:12,340::train::INFO] [train] Iter 564896 | loss 0.5041 | loss(rot) 0.0918 | loss(pos) 0.0340 | loss(seq) 0.3783 | grad 3.0650 | lr 0.0000 | time_forward 3.2190 | time_backward 4.1360 |
[2023-10-23 02:13:19,204::train::INFO] [train] Iter 564897 | loss 1.0693 | loss(rot) 0.4579 | loss(pos) 0.1295 | loss(seq) 0.4818 | grad 4.5717 | lr 0.0000 | time_forward 2.8680 | time_backward 3.9940 |
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