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[2023-10-23 01:53:18,610::train::INFO] [train] Iter 564688 | loss 1.2401 | loss(rot) 1.1708 | loss(pos) 0.0163 | loss(seq) 0.0530 | grad 6.0793 | lr 0.0000 | time_forward 3.3500 | time_backward 4.6500 |
[2023-10-23 01:53:26,568::train::INFO] [train] Iter 564689 | loss 0.7428 | loss(rot) 0.2721 | loss(pos) 0.1951 | loss(seq) 0.2756 | grad 3.5726 | lr 0.0000 | time_forward 3.2870 | time_backward 4.6670 |
[2023-10-23 01:53:29,334::train::INFO] [train] Iter 564690 | loss 0.9495 | loss(rot) 0.8071 | loss(pos) 0.0136 | loss(seq) 0.1288 | grad 3.6447 | lr 0.0000 | time_forward 1.3020 | time_backward 1.4610 |
[2023-10-23 01:53:37,280::train::INFO] [train] Iter 564691 | loss 0.3354 | loss(rot) 0.1172 | loss(pos) 0.0447 | loss(seq) 0.1736 | grad 2.2338 | lr 0.0000 | time_forward 3.4480 | time_backward 4.4750 |
[2023-10-23 01:53:44,035::train::INFO] [train] Iter 564692 | loss 1.4964 | loss(rot) 1.4539 | loss(pos) 0.0397 | loss(seq) 0.0028 | grad 7.1252 | lr 0.0000 | time_forward 2.9450 | time_backward 3.8070 |
[2023-10-23 01:53:46,805::train::INFO] [train] Iter 564693 | loss 1.8938 | loss(rot) 0.3557 | loss(pos) 1.5380 | loss(seq) 0.0001 | grad 16.6173 | lr 0.0000 | time_forward 1.3440 | time_backward 1.4220 |
[2023-10-23 01:53:53,842::train::INFO] [train] Iter 564694 | loss 1.9738 | loss(rot) 1.6012 | loss(pos) 0.0422 | loss(seq) 0.3304 | grad 5.2556 | lr 0.0000 | time_forward 3.0970 | time_backward 3.9360 |
[2023-10-23 01:54:01,158::train::INFO] [train] Iter 564695 | loss 0.1643 | loss(rot) 0.1232 | loss(pos) 0.0303 | loss(seq) 0.0107 | grad 1.9789 | lr 0.0000 | time_forward 3.1600 | time_backward 4.1520 |
[2023-10-23 01:54:08,367::train::INFO] [train] Iter 564696 | loss 0.3448 | loss(rot) 0.1028 | loss(pos) 0.1088 | loss(seq) 0.1332 | grad 4.9742 | lr 0.0000 | time_forward 3.1230 | time_backward 4.0830 |
[2023-10-23 01:54:16,350::train::INFO] [train] Iter 564697 | loss 0.6696 | loss(rot) 0.2112 | loss(pos) 0.4408 | loss(seq) 0.0176 | grad 6.3063 | lr 0.0000 | time_forward 3.3090 | time_backward 4.6720 |
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