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[2023-09-03 00:19:10,660::train::INFO] [train] Iter 15770 | loss 0.7430 | loss(rot) 0.5140 | loss(pos) 0.1942 | loss(seq) 0.0348 | grad 5.1459 | lr 0.0010 | time_forward 3.1890 | time_backward 4.4280 |
[2023-09-03 00:19:20,509::train::INFO] [train] Iter 15771 | loss 1.3152 | loss(rot) 0.0886 | loss(pos) 1.0309 | loss(seq) 0.1957 | grad 6.0212 | lr 0.0010 | time_forward 4.1400 | time_backward 5.7050 |
[2023-09-03 00:19:29,476::train::INFO] [train] Iter 15772 | loss 1.9614 | loss(rot) 1.7982 | loss(pos) 0.1628 | loss(seq) 0.0003 | grad 5.3449 | lr 0.0010 | time_forward 3.9220 | time_backward 5.0420 |
[2023-09-03 00:19:37,162::train::INFO] [train] Iter 15773 | loss 0.6802 | loss(rot) 0.5218 | loss(pos) 0.1082 | loss(seq) 0.0502 | grad 5.4163 | lr 0.0010 | time_forward 3.2010 | time_backward 4.4800 |
[2023-09-03 00:19:45,990::train::INFO] [train] Iter 15774 | loss 0.7837 | loss(rot) 0.2124 | loss(pos) 0.2758 | loss(seq) 0.2956 | grad 4.2224 | lr 0.0010 | time_forward 3.4610 | time_backward 5.3620 |
[2023-09-03 00:19:56,178::train::INFO] [train] Iter 15775 | loss 2.2461 | loss(rot) 1.2565 | loss(pos) 0.3947 | loss(seq) 0.5950 | grad 4.7246 | lr 0.0010 | time_forward 4.0470 | time_backward 6.1380 |
[2023-09-03 00:20:06,171::train::INFO] [train] Iter 15776 | loss 0.8405 | loss(rot) 0.5752 | loss(pos) 0.2652 | loss(seq) 0.0000 | grad 4.8112 | lr 0.0010 | time_forward 3.7470 | time_backward 6.2390 |
[2023-09-03 00:20:14,838::train::INFO] [train] Iter 15777 | loss 1.7731 | loss(rot) 1.6568 | loss(pos) 0.1163 | loss(seq) 0.0000 | grad 5.5001 | lr 0.0010 | time_forward 3.4010 | time_backward 5.2610 |
[2023-09-03 00:20:25,314::train::INFO] [train] Iter 15778 | loss 1.8445 | loss(rot) 1.6851 | loss(pos) 0.1229 | loss(seq) 0.0365 | grad 7.7593 | lr 0.0010 | time_forward 4.2780 | time_backward 6.1940 |
[2023-09-03 00:20:28,236::train::INFO] [train] Iter 15779 | loss 2.5115 | loss(rot) 2.2235 | loss(pos) 0.2832 | loss(seq) 0.0048 | grad 4.2938 | lr 0.0010 | time_forward 1.3190 | time_backward 1.6010 |
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