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[2023-10-25 12:10:20,768::train::INFO] [train] Iter 595657 | loss 0.3826 | loss(rot) 0.0321 | loss(pos) 0.0472 | loss(seq) 0.3033 | grad 2.4206 | lr 0.0000 | time_forward 3.7130 | time_backward 4.9790 |
[2023-10-25 12:10:23,567::train::INFO] [train] Iter 595658 | loss 0.2354 | loss(rot) 0.1697 | loss(pos) 0.0159 | loss(seq) 0.0498 | grad 3.4536 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4620 |
[2023-10-25 12:10:31,973::train::INFO] [train] Iter 595659 | loss 0.2322 | loss(rot) 0.2054 | loss(pos) 0.0256 | loss(seq) 0.0012 | grad 3.2298 | lr 0.0000 | time_forward 3.5440 | time_backward 4.8430 |
[2023-10-25 12:10:34,766::train::INFO] [train] Iter 595660 | loss 0.4001 | loss(rot) 0.3679 | loss(pos) 0.0255 | loss(seq) 0.0068 | grad 3.8111 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4530 |
[2023-10-25 12:10:43,470::train::INFO] [train] Iter 595661 | loss 0.4235 | loss(rot) 0.1893 | loss(pos) 0.0329 | loss(seq) 0.2012 | grad 2.6068 | lr 0.0000 | time_forward 3.6650 | time_backward 5.0120 |
[2023-10-25 12:10:53,430::train::INFO] [train] Iter 595662 | loss 0.4375 | loss(rot) 0.3965 | loss(pos) 0.0309 | loss(seq) 0.0101 | grad 2.3378 | lr 0.0000 | time_forward 4.0440 | time_backward 5.9120 |
[2023-10-25 12:10:56,216::train::INFO] [train] Iter 595663 | loss 0.6373 | loss(rot) 0.0942 | loss(pos) 0.0421 | loss(seq) 0.5009 | grad 3.0146 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4580 |
[2023-10-25 12:10:59,030::train::INFO] [train] Iter 595664 | loss 0.8260 | loss(rot) 0.4362 | loss(pos) 0.1608 | loss(seq) 0.2290 | grad 3.5791 | lr 0.0000 | time_forward 1.3630 | time_backward 1.4470 |
[2023-10-25 12:11:09,208::train::INFO] [train] Iter 595665 | loss 0.2748 | loss(rot) 0.0555 | loss(pos) 0.1850 | loss(seq) 0.0343 | grad 3.2696 | lr 0.0000 | time_forward 4.0620 | time_backward 6.0770 |
[2023-10-25 12:11:11,987::train::INFO] [train] Iter 595666 | loss 0.5958 | loss(rot) 0.5557 | loss(pos) 0.0401 | loss(seq) 0.0000 | grad 3.2602 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4640 |
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