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[2023-10-25 02:46:03,205::train::INFO] [train] Iter 590662 | loss 1.7659 | loss(rot) 1.5329 | loss(pos) 0.0576 | loss(seq) 0.1754 | grad 3.3122 | lr 0.0000 | time_forward 3.3530 | time_backward 4.5970 |
[2023-10-25 02:46:11,534::train::INFO] [train] Iter 590663 | loss 0.5867 | loss(rot) 0.0793 | loss(pos) 0.0342 | loss(seq) 0.4732 | grad 2.4272 | lr 0.0000 | time_forward 3.5540 | time_backward 4.7710 |
[2023-10-25 02:46:13,794::train::INFO] [train] Iter 590664 | loss 0.7035 | loss(rot) 0.5101 | loss(pos) 0.0395 | loss(seq) 0.1539 | grad 4.0829 | lr 0.0000 | time_forward 1.0500 | time_backward 1.2070 |
[2023-10-25 02:46:21,918::train::INFO] [train] Iter 590665 | loss 3.5686 | loss(rot) 2.7178 | loss(pos) 0.4624 | loss(seq) 0.3884 | grad 4.7466 | lr 0.0000 | time_forward 3.5440 | time_backward 4.5760 |
[2023-10-25 02:46:24,175::train::INFO] [train] Iter 590666 | loss 0.7639 | loss(rot) 0.4004 | loss(pos) 0.0853 | loss(seq) 0.2781 | grad 3.0586 | lr 0.0000 | time_forward 1.0340 | time_backward 1.2210 |
[2023-10-25 02:46:32,027::train::INFO] [train] Iter 590667 | loss 1.9026 | loss(rot) 1.8195 | loss(pos) 0.0712 | loss(seq) 0.0119 | grad 8.5915 | lr 0.0000 | time_forward 3.2800 | time_backward 4.5680 |
[2023-10-25 02:46:41,066::train::INFO] [train] Iter 590668 | loss 0.7034 | loss(rot) 0.6720 | loss(pos) 0.0157 | loss(seq) 0.0157 | grad 5.2833 | lr 0.0000 | time_forward 3.7960 | time_backward 5.2410 |
[2023-10-25 02:46:48,307::train::INFO] [train] Iter 590669 | loss 1.0772 | loss(rot) 1.0174 | loss(pos) 0.0597 | loss(seq) 0.0001 | grad 2.7154 | lr 0.0000 | time_forward 3.0670 | time_backward 4.1700 |
[2023-10-25 02:46:56,571::train::INFO] [train] Iter 590670 | loss 0.4774 | loss(rot) 0.4514 | loss(pos) 0.0243 | loss(seq) 0.0016 | grad 3.7421 | lr 0.0000 | time_forward 3.5240 | time_backward 4.7360 |
[2023-10-25 02:47:05,975::train::INFO] [train] Iter 590671 | loss 1.1379 | loss(rot) 1.0274 | loss(pos) 0.0180 | loss(seq) 0.0925 | grad 3.3117 | lr 0.0000 | time_forward 3.7280 | time_backward 5.6730 |
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