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[2023-10-23 12:29:53,143::train::INFO] [train] Iter 570782 | loss 0.5075 | loss(rot) 0.4935 | loss(pos) 0.0139 | loss(seq) 0.0001 | grad 9.0588 | lr 0.0000 | time_forward 3.6080 | time_backward 5.1280 |
[2023-10-23 12:30:01,052::train::INFO] [train] Iter 570783 | loss 1.0528 | loss(rot) 0.8229 | loss(pos) 0.1932 | loss(seq) 0.0367 | grad 20.5267 | lr 0.0000 | time_forward 3.4040 | time_backward 4.5020 |
[2023-10-23 12:30:09,873::train::INFO] [train] Iter 570784 | loss 0.9005 | loss(rot) 0.3296 | loss(pos) 0.1669 | loss(seq) 0.4039 | grad 2.5435 | lr 0.0000 | time_forward 3.7830 | time_backward 5.0360 |
[2023-10-23 12:30:12,247::train::INFO] [train] Iter 570785 | loss 1.6029 | loss(rot) 1.4416 | loss(pos) 0.0833 | loss(seq) 0.0780 | grad 4.7236 | lr 0.0000 | time_forward 1.1100 | time_backward 1.2600 |
[2023-10-23 12:30:15,086::train::INFO] [train] Iter 570786 | loss 0.4126 | loss(rot) 0.0211 | loss(pos) 0.3863 | loss(seq) 0.0052 | grad 9.9908 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4910 |
[2023-10-23 12:30:23,815::train::INFO] [train] Iter 570787 | loss 0.5627 | loss(rot) 0.5335 | loss(pos) 0.0272 | loss(seq) 0.0019 | grad 26.6772 | lr 0.0000 | time_forward 3.6120 | time_backward 5.1130 |
[2023-10-23 12:30:26,111::train::INFO] [train] Iter 570788 | loss 0.8190 | loss(rot) 0.4284 | loss(pos) 0.0419 | loss(seq) 0.3486 | grad 3.2935 | lr 0.0000 | time_forward 1.0540 | time_backward 1.2390 |
[2023-10-23 12:30:28,834::train::INFO] [train] Iter 570789 | loss 0.4136 | loss(rot) 0.0294 | loss(pos) 0.3721 | loss(seq) 0.0121 | grad 6.2458 | lr 0.0000 | time_forward 1.2640 | time_backward 1.4550 |
[2023-10-23 12:30:31,737::train::INFO] [train] Iter 570790 | loss 0.4754 | loss(rot) 0.2058 | loss(pos) 0.0176 | loss(seq) 0.2521 | grad 3.4764 | lr 0.0000 | time_forward 1.3180 | time_backward 1.5820 |
[2023-10-23 12:30:38,929::train::INFO] [train] Iter 570791 | loss 0.5144 | loss(rot) 0.1224 | loss(pos) 0.0774 | loss(seq) 0.3146 | grad 3.5620 | lr 0.0000 | time_forward 3.0930 | time_backward 4.0940 |
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