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[2023-10-25 10:09:25,757::train::INFO] [train] Iter 594658 | loss 0.7985 | loss(rot) 0.4764 | loss(pos) 0.1077 | loss(seq) 0.2144 | grad 3.4008 | lr 0.0000 | time_forward 1.1900 | time_backward 1.3870 |
[2023-10-25 10:09:32,983::train::INFO] [train] Iter 594659 | loss 0.3537 | loss(rot) 0.1190 | loss(pos) 0.0433 | loss(seq) 0.1913 | grad 2.4972 | lr 0.0000 | time_forward 3.1080 | time_backward 4.1160 |
[2023-10-25 10:09:41,583::train::INFO] [train] Iter 594660 | loss 1.8958 | loss(rot) 1.3697 | loss(pos) 0.0470 | loss(seq) 0.4792 | grad 7.7353 | lr 0.0000 | time_forward 3.5830 | time_backward 5.0130 |
[2023-10-25 10:09:44,344::train::INFO] [train] Iter 594661 | loss 0.8272 | loss(rot) 0.6426 | loss(pos) 0.0231 | loss(seq) 0.1615 | grad 2.4148 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4420 |
[2023-10-25 10:09:47,123::train::INFO] [train] Iter 594662 | loss 0.5405 | loss(rot) 0.1776 | loss(pos) 0.1005 | loss(seq) 0.2624 | grad 3.0428 | lr 0.0000 | time_forward 1.3590 | time_backward 1.4170 |
[2023-10-25 10:09:56,424::train::INFO] [train] Iter 594663 | loss 0.3095 | loss(rot) 0.2458 | loss(pos) 0.0350 | loss(seq) 0.0287 | grad 2.0216 | lr 0.0000 | time_forward 4.0250 | time_backward 5.2720 |
[2023-10-25 10:10:05,350::train::INFO] [train] Iter 594664 | loss 0.3965 | loss(rot) 0.0566 | loss(pos) 0.0329 | loss(seq) 0.3069 | grad 1.6773 | lr 0.0000 | time_forward 3.8340 | time_backward 5.0890 |
[2023-10-25 10:10:15,388::train::INFO] [train] Iter 594665 | loss 0.3963 | loss(rot) 0.2198 | loss(pos) 0.0330 | loss(seq) 0.1434 | grad 2.2884 | lr 0.0000 | time_forward 4.0240 | time_backward 6.0110 |
[2023-10-25 10:10:18,146::train::INFO] [train] Iter 594666 | loss 1.0276 | loss(rot) 0.9291 | loss(pos) 0.0241 | loss(seq) 0.0743 | grad 20.9871 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4390 |
[2023-10-25 10:10:26,585::train::INFO] [train] Iter 594667 | loss 3.0392 | loss(rot) 2.1988 | loss(pos) 0.3414 | loss(seq) 0.4990 | grad 5.4193 | lr 0.0000 | time_forward 3.5330 | time_backward 4.8820 |
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