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[2023-09-03 04:07:43,841::train::INFO] [train] Iter 17768 | loss 0.9741 | loss(rot) 0.1731 | loss(pos) 0.7936 | loss(seq) 0.0074 | grad 4.2156 | lr 0.0010 | time_forward 3.2650 | time_backward 4.4590 |
[2023-09-03 04:07:46,513::train::INFO] [train] Iter 17769 | loss 1.1106 | loss(rot) 0.4260 | loss(pos) 0.3442 | loss(seq) 0.3404 | grad 4.7212 | lr 0.0010 | time_forward 1.2500 | time_backward 1.4180 |
[2023-09-03 04:07:49,193::train::INFO] [train] Iter 17770 | loss 0.6668 | loss(rot) 0.1495 | loss(pos) 0.4322 | loss(seq) 0.0851 | grad 5.0529 | lr 0.0010 | time_forward 1.2580 | time_backward 1.4060 |
[2023-09-03 04:07:51,399::train::INFO] [train] Iter 17771 | loss 1.1855 | loss(rot) 0.1848 | loss(pos) 0.7306 | loss(seq) 0.2701 | grad 6.4863 | lr 0.0010 | time_forward 1.0440 | time_backward 1.1580 |
[2023-09-03 04:07:59,542::train::INFO] [train] Iter 17772 | loss 3.2013 | loss(rot) 2.4240 | loss(pos) 0.3052 | loss(seq) 0.4722 | grad 5.6208 | lr 0.0010 | time_forward 3.5360 | time_backward 4.6040 |
[2023-09-03 04:08:07,616::train::INFO] [train] Iter 17773 | loss 1.0041 | loss(rot) 0.4233 | loss(pos) 0.4433 | loss(seq) 0.1374 | grad 9.3121 | lr 0.0010 | time_forward 3.3100 | time_backward 4.7610 |
[2023-09-03 04:08:15,434::train::INFO] [train] Iter 17774 | loss 0.9324 | loss(rot) 0.2087 | loss(pos) 0.4951 | loss(seq) 0.2286 | grad 4.0753 | lr 0.0010 | time_forward 3.0820 | time_backward 4.7320 |
[2023-09-03 04:08:23,255::train::INFO] [train] Iter 17775 | loss 1.0205 | loss(rot) 0.9034 | loss(pos) 0.1140 | loss(seq) 0.0031 | grad 9.0718 | lr 0.0010 | time_forward 3.1730 | time_backward 4.6450 |
[2023-09-03 04:08:30,967::train::INFO] [train] Iter 17776 | loss 0.9557 | loss(rot) 0.2521 | loss(pos) 0.6702 | loss(seq) 0.0334 | grad 5.4528 | lr 0.0010 | time_forward 3.3270 | time_backward 4.3810 |
[2023-09-03 04:08:38,648::train::INFO] [train] Iter 17777 | loss 0.7315 | loss(rot) 0.2386 | loss(pos) 0.3866 | loss(seq) 0.1063 | grad 4.7227 | lr 0.0010 | time_forward 3.3630 | time_backward 4.3140 |
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