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[2023-10-22 14:15:48,375::train::INFO] [train] Iter 558494 | loss 0.3011 | loss(rot) 0.1501 | loss(pos) 0.0337 | loss(seq) 0.1173 | grad 7.4387 | lr 0.0000 | time_forward 4.0150 | time_backward 5.9210 |
[2023-10-22 14:15:59,032::train::INFO] [train] Iter 558495 | loss 1.1900 | loss(rot) 0.9197 | loss(pos) 0.0754 | loss(seq) 0.1949 | grad 3.1062 | lr 0.0000 | time_forward 4.2750 | time_backward 6.3780 |
[2023-10-22 14:16:10,185::train::INFO] [train] Iter 558496 | loss 0.3505 | loss(rot) 0.0974 | loss(pos) 0.0707 | loss(seq) 0.1824 | grad 2.2770 | lr 0.0000 | time_forward 4.7590 | time_backward 6.3910 |
[2023-10-22 14:16:20,876::train::INFO] [train] Iter 558497 | loss 0.5543 | loss(rot) 0.2648 | loss(pos) 0.0321 | loss(seq) 0.2575 | grad 3.6245 | lr 0.0000 | time_forward 4.2250 | time_backward 6.4630 |
[2023-10-22 14:16:31,931::train::INFO] [train] Iter 558498 | loss 0.5943 | loss(rot) 0.5306 | loss(pos) 0.0293 | loss(seq) 0.0345 | grad 3.2552 | lr 0.0000 | time_forward 4.8030 | time_backward 6.2490 |
[2023-10-22 14:16:42,023::train::INFO] [train] Iter 558499 | loss 0.8323 | loss(rot) 0.2585 | loss(pos) 0.5661 | loss(seq) 0.0077 | grad 6.7004 | lr 0.0000 | time_forward 4.2360 | time_backward 5.8530 |
[2023-10-22 14:16:50,981::train::INFO] [train] Iter 558500 | loss 0.3917 | loss(rot) 0.3559 | loss(pos) 0.0271 | loss(seq) 0.0087 | grad 4.0863 | lr 0.0000 | time_forward 3.6750 | time_backward 5.2780 |
[2023-10-22 14:16:53,278::train::INFO] [train] Iter 558501 | loss 0.8230 | loss(rot) 0.5576 | loss(pos) 0.1684 | loss(seq) 0.0970 | grad 3.8700 | lr 0.0000 | time_forward 1.0720 | time_backward 1.2210 |
[2023-10-22 14:16:56,060::train::INFO] [train] Iter 558502 | loss 0.5599 | loss(rot) 0.2726 | loss(pos) 0.0537 | loss(seq) 0.2336 | grad 3.4696 | lr 0.0000 | time_forward 1.3770 | time_backward 1.4000 |
[2023-10-22 14:17:00,837::train::INFO] [train] Iter 558503 | loss 0.6066 | loss(rot) 0.4787 | loss(pos) 0.0218 | loss(seq) 0.1061 | grad 2.3227 | lr 0.0000 | time_forward 2.1350 | time_backward 2.6400 |
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