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[2023-10-23 03:23:31,397::train::INFO] [train] Iter 565587 | loss 1.3563 | loss(rot) 1.1326 | loss(pos) 0.0344 | loss(seq) 0.1893 | grad 3.7823 | lr 0.0000 | time_forward 2.7370 | time_backward 3.7540 |
[2023-10-23 03:23:37,491::train::INFO] [train] Iter 565588 | loss 0.7419 | loss(rot) 0.3642 | loss(pos) 0.0796 | loss(seq) 0.2981 | grad 3.6214 | lr 0.0000 | time_forward 2.6530 | time_backward 3.4380 |
[2023-10-23 03:23:42,373::train::INFO] [train] Iter 565589 | loss 0.5805 | loss(rot) 0.0588 | loss(pos) 0.1099 | loss(seq) 0.4118 | grad 3.2201 | lr 0.0000 | time_forward 2.1050 | time_backward 2.7740 |
[2023-10-23 03:23:49,481::train::INFO] [train] Iter 565590 | loss 0.3893 | loss(rot) 0.0356 | loss(pos) 0.1309 | loss(seq) 0.2229 | grad 3.7444 | lr 0.0000 | time_forward 3.0800 | time_backward 4.0250 |
[2023-10-23 03:23:52,058::train::INFO] [train] Iter 565591 | loss 2.4597 | loss(rot) 1.6072 | loss(pos) 0.2256 | loss(seq) 0.6269 | grad 6.7694 | lr 0.0000 | time_forward 1.1900 | time_backward 1.3840 |
[2023-10-23 03:23:54,293::train::INFO] [train] Iter 565592 | loss 1.0420 | loss(rot) 0.5615 | loss(pos) 0.1372 | loss(seq) 0.3434 | grad 4.0589 | lr 0.0000 | time_forward 1.0390 | time_backward 1.1910 |
[2023-10-23 03:23:56,966::train::INFO] [train] Iter 565593 | loss 1.0753 | loss(rot) 0.4129 | loss(pos) 0.2791 | loss(seq) 0.3833 | grad 4.5515 | lr 0.0000 | time_forward 1.2630 | time_backward 1.4010 |
[2023-10-23 03:24:03,485::train::INFO] [train] Iter 565594 | loss 1.7673 | loss(rot) 1.7300 | loss(pos) 0.0320 | loss(seq) 0.0053 | grad 3.7728 | lr 0.0000 | time_forward 2.7830 | time_backward 3.7330 |
[2023-10-23 03:24:06,141::train::INFO] [train] Iter 565595 | loss 0.6527 | loss(rot) 0.6211 | loss(pos) 0.0316 | loss(seq) 0.0000 | grad 5.1143 | lr 0.0000 | time_forward 1.2480 | time_backward 1.4050 |
[2023-10-23 03:24:08,838::train::INFO] [train] Iter 565596 | loss 0.3154 | loss(rot) 0.0910 | loss(pos) 0.1859 | loss(seq) 0.0384 | grad 5.5916 | lr 0.0000 | time_forward 1.2800 | time_backward 1.4120 |
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