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[2023-09-01 23:50:26,313::train::INFO] [train] Iter 03682 | loss 1.6587 | loss(rot) 1.0821 | loss(pos) 0.3233 | loss(seq) 0.2533 | grad 4.9537 | lr 0.0010 | time_forward 1.2340 | time_backward 1.4170 |
[2023-09-01 23:50:35,801::train::INFO] [train] Iter 03683 | loss 1.3105 | loss(rot) 0.2844 | loss(pos) 0.6449 | loss(seq) 0.3812 | grad 4.1933 | lr 0.0010 | time_forward 4.1500 | time_backward 5.3340 |
[2023-09-01 23:50:44,833::train::INFO] [train] Iter 03684 | loss 0.8322 | loss(rot) 0.4266 | loss(pos) 0.2439 | loss(seq) 0.1618 | grad 2.7403 | lr 0.0010 | time_forward 3.3500 | time_backward 5.6790 |
[2023-09-01 23:51:04,377::train::INFO] [train] Iter 03685 | loss 1.8812 | loss(rot) 1.6924 | loss(pos) 0.1887 | loss(seq) 0.0001 | grad 4.1575 | lr 0.0010 | time_forward 10.7080 | time_backward 8.8340 |
[2023-09-01 23:51:18,924::train::INFO] [train] Iter 03686 | loss 3.1485 | loss(rot) 2.6778 | loss(pos) 0.2075 | loss(seq) 0.2633 | grad 5.0500 | lr 0.0010 | time_forward 9.0730 | time_backward 5.4690 |
[2023-09-01 23:51:28,045::train::INFO] [train] Iter 03687 | loss 2.2203 | loss(rot) 1.6831 | loss(pos) 0.1699 | loss(seq) 0.3673 | grad 4.8898 | lr 0.0010 | time_forward 3.8440 | time_backward 5.2730 |
[2023-09-01 23:51:38,031::train::INFO] [train] Iter 03688 | loss 2.8014 | loss(rot) 2.5643 | loss(pos) 0.2372 | loss(seq) 0.0000 | grad 3.2895 | lr 0.0010 | time_forward 4.1720 | time_backward 5.8100 |
[2023-09-01 23:51:47,971::train::INFO] [train] Iter 03689 | loss 1.6683 | loss(rot) 0.1740 | loss(pos) 1.4838 | loss(seq) 0.0105 | grad 5.4392 | lr 0.0010 | time_forward 4.1340 | time_backward 5.8030 |
[2023-09-01 23:51:56,325::train::INFO] [train] Iter 03690 | loss 4.0401 | loss(rot) 0.0109 | loss(pos) 4.0292 | loss(seq) 0.0000 | grad 5.5282 | lr 0.0010 | time_forward 3.4700 | time_backward 4.8810 |
[2023-09-01 23:52:04,295::train::INFO] [train] Iter 03691 | loss 3.1994 | loss(rot) 2.6712 | loss(pos) 0.3069 | loss(seq) 0.2213 | grad 4.9562 | lr 0.0010 | time_forward 3.2980 | time_backward 4.6680 |
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