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[2023-10-24 23:19:20,125::train::INFO] [train] Iter 588864 | loss 0.9460 | loss(rot) 0.3647 | loss(pos) 0.1887 | loss(seq) 0.3926 | grad 5.4109 | lr 0.0000 | time_forward 3.9150 | time_backward 5.6500 |
[2023-10-24 23:19:30,498::train::INFO] [train] Iter 588865 | loss 0.5103 | loss(rot) 0.0706 | loss(pos) 0.4370 | loss(seq) 0.0027 | grad 4.0038 | lr 0.0000 | time_forward 4.3140 | time_backward 6.0560 |
[2023-10-24 23:19:39,203::train::INFO] [train] Iter 588866 | loss 0.2885 | loss(rot) 0.2606 | loss(pos) 0.0280 | loss(seq) 0.0000 | grad 3.9627 | lr 0.0000 | time_forward 3.7210 | time_backward 4.9810 |
[2023-10-24 23:19:44,982::train::INFO] [train] Iter 588867 | loss 0.8461 | loss(rot) 0.8290 | loss(pos) 0.0171 | loss(seq) 0.0000 | grad 2.7452 | lr 0.0000 | time_forward 2.4770 | time_backward 3.2990 |
[2023-10-24 23:19:54,194::train::INFO] [train] Iter 588868 | loss 1.4954 | loss(rot) 1.3054 | loss(pos) 0.0346 | loss(seq) 0.1554 | grad 5.6698 | lr 0.0000 | time_forward 3.8960 | time_backward 5.3120 |
[2023-10-24 23:20:01,617::train::INFO] [train] Iter 588869 | loss 0.3941 | loss(rot) 0.1088 | loss(pos) 0.0369 | loss(seq) 0.2484 | grad 2.8682 | lr 0.0000 | time_forward 3.1320 | time_backward 4.2890 |
[2023-10-24 23:20:11,204::train::INFO] [train] Iter 588870 | loss 1.7891 | loss(rot) 1.2246 | loss(pos) 0.2365 | loss(seq) 0.3281 | grad 6.5938 | lr 0.0000 | time_forward 3.7500 | time_backward 5.8340 |
[2023-10-24 23:20:20,788::train::INFO] [train] Iter 588871 | loss 1.7657 | loss(rot) 1.2297 | loss(pos) 0.1360 | loss(seq) 0.4000 | grad 5.8612 | lr 0.0000 | time_forward 3.9230 | time_backward 5.6580 |
[2023-10-24 23:20:29,217::train::INFO] [train] Iter 588872 | loss 0.5090 | loss(rot) 0.0286 | loss(pos) 0.2078 | loss(seq) 0.2726 | grad 3.6584 | lr 0.0000 | time_forward 3.5550 | time_backward 4.8700 |
[2023-10-24 23:20:36,349::train::INFO] [train] Iter 588873 | loss 1.4564 | loss(rot) 1.0868 | loss(pos) 0.0537 | loss(seq) 0.3159 | grad 11.4878 | lr 0.0000 | time_forward 3.2470 | time_backward 3.8820 |
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