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[2023-10-24 10:19:02,575::train::INFO] [train] Iter 582070 | loss 0.7587 | loss(rot) 0.4367 | loss(pos) 0.0546 | loss(seq) 0.2674 | grad 2.8914 | lr 0.0000 | time_forward 3.6390 | time_backward 5.3170 |
[2023-10-24 10:19:10,177::train::INFO] [train] Iter 582071 | loss 0.9957 | loss(rot) 0.0090 | loss(pos) 0.9864 | loss(seq) 0.0003 | grad 14.8696 | lr 0.0000 | time_forward 3.2290 | time_backward 4.3700 |
[2023-10-24 10:19:20,043::train::INFO] [train] Iter 582072 | loss 0.5615 | loss(rot) 0.2173 | loss(pos) 0.0516 | loss(seq) 0.2926 | grad 3.0019 | lr 0.0000 | time_forward 4.0210 | time_backward 5.8420 |
[2023-10-24 10:19:29,388::train::INFO] [train] Iter 582073 | loss 0.3996 | loss(rot) 0.1593 | loss(pos) 0.0614 | loss(seq) 0.1788 | grad 3.0056 | lr 0.0000 | time_forward 3.8850 | time_backward 5.4570 |
[2023-10-24 10:19:39,560::train::INFO] [train] Iter 582074 | loss 1.8227 | loss(rot) 1.7915 | loss(pos) 0.0300 | loss(seq) 0.0012 | grad 26.3789 | lr 0.0000 | time_forward 4.6210 | time_backward 5.5480 |
[2023-10-24 10:19:49,448::train::INFO] [train] Iter 582075 | loss 0.4701 | loss(rot) 0.1164 | loss(pos) 0.0215 | loss(seq) 0.3321 | grad 2.5109 | lr 0.0000 | time_forward 4.5730 | time_backward 5.3110 |
[2023-10-24 10:19:57,822::train::INFO] [train] Iter 582076 | loss 0.4925 | loss(rot) 0.1465 | loss(pos) 0.0338 | loss(seq) 0.3122 | grad 2.7824 | lr 0.0000 | time_forward 3.6880 | time_backward 4.6820 |
[2023-10-24 10:20:00,150::train::INFO] [train] Iter 582077 | loss 0.8262 | loss(rot) 0.8052 | loss(pos) 0.0171 | loss(seq) 0.0039 | grad 14.6761 | lr 0.0000 | time_forward 1.0810 | time_backward 1.2440 |
[2023-10-24 10:20:09,403::train::INFO] [train] Iter 582078 | loss 0.4289 | loss(rot) 0.0927 | loss(pos) 0.0438 | loss(seq) 0.2923 | grad 2.3198 | lr 0.0000 | time_forward 3.9410 | time_backward 5.3100 |
[2023-10-24 10:20:15,846::train::INFO] [train] Iter 582079 | loss 2.6943 | loss(rot) 1.8495 | loss(pos) 0.3521 | loss(seq) 0.4928 | grad 11.5345 | lr 0.0000 | time_forward 2.6950 | time_backward 3.7440 |
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