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[2023-10-22 22:59:30,533::train::INFO] [train] Iter 562990 | loss 0.9486 | loss(rot) 0.2803 | loss(pos) 0.4645 | loss(seq) 0.2038 | grad 3.7562 | lr 0.0000 | time_forward 1.4120 | time_backward 1.6570 |
[2023-10-22 22:59:33,226::train::INFO] [train] Iter 562991 | loss 0.3685 | loss(rot) 0.2123 | loss(pos) 0.1017 | loss(seq) 0.0545 | grad 2.4303 | lr 0.0000 | time_forward 1.2880 | time_backward 1.3910 |
[2023-10-22 22:59:39,433::train::INFO] [train] Iter 562992 | loss 1.5421 | loss(rot) 1.4247 | loss(pos) 0.0399 | loss(seq) 0.0776 | grad 4.9593 | lr 0.0000 | time_forward 2.7310 | time_backward 3.4480 |
[2023-10-22 22:59:46,620::train::INFO] [train] Iter 562993 | loss 0.7900 | loss(rot) 0.5584 | loss(pos) 0.0271 | loss(seq) 0.2046 | grad 5.6415 | lr 0.0000 | time_forward 3.1710 | time_backward 4.0130 |
[2023-10-22 22:59:49,034::train::INFO] [train] Iter 562994 | loss 0.1620 | loss(rot) 0.0773 | loss(pos) 0.0556 | loss(seq) 0.0291 | grad 3.1694 | lr 0.0000 | time_forward 1.1830 | time_backward 1.2280 |
[2023-10-22 22:59:56,779::train::INFO] [train] Iter 562995 | loss 0.5325 | loss(rot) 0.5025 | loss(pos) 0.0145 | loss(seq) 0.0154 | grad 3.2453 | lr 0.0000 | time_forward 3.2360 | time_backward 4.5070 |
[2023-10-22 23:00:03,368::train::INFO] [train] Iter 562996 | loss 0.5464 | loss(rot) 0.5066 | loss(pos) 0.0393 | loss(seq) 0.0005 | grad 4.6372 | lr 0.0000 | time_forward 2.8800 | time_backward 3.7050 |
[2023-10-22 23:00:10,615::train::INFO] [train] Iter 562997 | loss 0.5300 | loss(rot) 0.0662 | loss(pos) 0.4548 | loss(seq) 0.0090 | grad 4.5503 | lr 0.0000 | time_forward 3.1730 | time_backward 4.0700 |
[2023-10-22 23:00:17,316::train::INFO] [train] Iter 562998 | loss 0.3746 | loss(rot) 0.1423 | loss(pos) 0.0423 | loss(seq) 0.1900 | grad 2.8024 | lr 0.0000 | time_forward 2.9420 | time_backward 3.7560 |
[2023-10-22 23:00:19,527::train::INFO] [train] Iter 562999 | loss 0.6695 | loss(rot) 0.2916 | loss(pos) 0.2356 | loss(seq) 0.1423 | grad 4.5781 | lr 0.0000 | time_forward 1.0230 | time_backward 1.1840 |
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