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[2023-09-02 14:03:16,799::train::INFO] [train] Iter 10676 | loss 1.5318 | loss(rot) 0.2836 | loss(pos) 0.7747 | loss(seq) 0.4735 | grad 4.4265 | lr 0.0010 | time_forward 4.4220 | time_backward 6.1210 |
[2023-09-02 14:03:19,484::train::INFO] [train] Iter 10677 | loss 1.9401 | loss(rot) 1.7944 | loss(pos) 0.0462 | loss(seq) 0.0996 | grad 4.7942 | lr 0.0010 | time_forward 1.2430 | time_backward 1.4390 |
[2023-09-02 14:03:27,166::train::INFO] [train] Iter 10678 | loss 0.8371 | loss(rot) 0.3686 | loss(pos) 0.4093 | loss(seq) 0.0592 | grad 4.7093 | lr 0.0010 | time_forward 3.0790 | time_backward 4.5990 |
[2023-09-02 14:03:35,782::train::INFO] [train] Iter 10679 | loss 1.8641 | loss(rot) 1.6475 | loss(pos) 0.2018 | loss(seq) 0.0147 | grad 10.0215 | lr 0.0010 | time_forward 3.6540 | time_backward 4.9590 |
[2023-09-02 14:03:42,899::train::INFO] [train] Iter 10680 | loss 2.4259 | loss(rot) 2.2635 | loss(pos) 0.1004 | loss(seq) 0.0620 | grad 6.5792 | lr 0.0010 | time_forward 3.0270 | time_backward 4.0860 |
[2023-09-02 14:03:45,696::train::INFO] [train] Iter 10681 | loss 1.3704 | loss(rot) 0.5928 | loss(pos) 0.2642 | loss(seq) 0.5134 | grad 4.6707 | lr 0.0010 | time_forward 1.2590 | time_backward 1.5340 |
[2023-09-02 14:03:56,484::train::INFO] [train] Iter 10682 | loss 1.7634 | loss(rot) 0.5288 | loss(pos) 0.5129 | loss(seq) 0.7217 | grad 4.4799 | lr 0.0010 | time_forward 4.4050 | time_backward 6.3790 |
[2023-09-02 14:03:59,251::train::INFO] [train] Iter 10683 | loss 2.3717 | loss(rot) 1.5709 | loss(pos) 0.4183 | loss(seq) 0.3825 | grad 6.8515 | lr 0.0010 | time_forward 1.2940 | time_backward 1.4700 |
[2023-09-02 14:04:08,079::train::INFO] [train] Iter 10684 | loss 0.9418 | loss(rot) 0.4820 | loss(pos) 0.3936 | loss(seq) 0.0662 | grad 5.4111 | lr 0.0010 | time_forward 3.7180 | time_backward 5.1070 |
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