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[2023-10-25 12:33:32,598::train::INFO] [train] Iter 595857 | loss 1.3223 | loss(rot) 0.8202 | loss(pos) 0.0496 | loss(seq) 0.4525 | grad 5.1891 | lr 0.0000 | time_forward 4.2770 | time_backward 5.3140 |
[2023-10-25 12:33:43,009::train::INFO] [train] Iter 595858 | loss 0.6335 | loss(rot) 0.3733 | loss(pos) 0.0353 | loss(seq) 0.2250 | grad 3.1739 | lr 0.0000 | time_forward 4.2430 | time_backward 6.1650 |
[2023-10-25 12:33:51,099::train::INFO] [train] Iter 595859 | loss 0.2156 | loss(rot) 0.1956 | loss(pos) 0.0196 | loss(seq) 0.0003 | grad 3.7247 | lr 0.0000 | time_forward 3.4970 | time_backward 4.5900 |
[2023-10-25 12:34:01,333::train::INFO] [train] Iter 595860 | loss 0.5790 | loss(rot) 0.0436 | loss(pos) 0.5332 | loss(seq) 0.0022 | grad 10.7781 | lr 0.0000 | time_forward 4.1750 | time_backward 6.0570 |
[2023-10-25 12:34:09,957::train::INFO] [train] Iter 595861 | loss 3.9702 | loss(rot) 0.0030 | loss(pos) 3.9673 | loss(seq) 0.0000 | grad 21.7045 | lr 0.0000 | time_forward 3.5370 | time_backward 5.0830 |
[2023-10-25 12:34:12,894::train::INFO] [train] Iter 595862 | loss 1.0213 | loss(rot) 0.5043 | loss(pos) 0.1528 | loss(seq) 0.3642 | grad 4.3076 | lr 0.0000 | time_forward 1.3540 | time_backward 1.5800 |
[2023-10-25 12:34:21,681::train::INFO] [train] Iter 595863 | loss 1.1964 | loss(rot) 1.0102 | loss(pos) 0.0539 | loss(seq) 0.1323 | grad 4.7065 | lr 0.0000 | time_forward 3.9820 | time_backward 4.8010 |
[2023-10-25 12:34:24,466::train::INFO] [train] Iter 595864 | loss 0.3392 | loss(rot) 0.1314 | loss(pos) 0.2015 | loss(seq) 0.0063 | grad 3.1477 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4890 |
[2023-10-25 12:34:32,772::train::INFO] [train] Iter 595865 | loss 0.6518 | loss(rot) 0.0269 | loss(pos) 0.4948 | loss(seq) 0.1302 | grad 6.2496 | lr 0.0000 | time_forward 3.4690 | time_backward 4.8220 |
[2023-10-25 12:34:42,915::train::INFO] [train] Iter 595866 | loss 0.6961 | loss(rot) 0.5927 | loss(pos) 0.0657 | loss(seq) 0.0377 | grad 3.2737 | lr 0.0000 | time_forward 4.2950 | time_backward 5.8440 |
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