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[2023-09-02 00:15:33,024::train::INFO] [train] Iter 03882 | loss 2.8872 | loss(rot) 2.2657 | loss(pos) 0.1555 | loss(seq) 0.4660 | grad 4.9178 | lr 0.0010 | time_forward 3.6520 | time_backward 5.1940 |
[2023-09-02 00:15:43,334::train::INFO] [train] Iter 03883 | loss 2.2097 | loss(rot) 0.8199 | loss(pos) 1.1226 | loss(seq) 0.2673 | grad 7.2577 | lr 0.0010 | time_forward 4.2180 | time_backward 6.0880 |
[2023-09-02 00:15:51,729::train::INFO] [train] Iter 03884 | loss 2.2497 | loss(rot) 1.2810 | loss(pos) 0.3359 | loss(seq) 0.6328 | grad 4.2394 | lr 0.0010 | time_forward 3.4860 | time_backward 4.9050 |
[2023-09-02 00:16:01,754::train::INFO] [train] Iter 03885 | loss 2.3822 | loss(rot) 1.4414 | loss(pos) 0.4736 | loss(seq) 0.4672 | grad 7.0645 | lr 0.0010 | time_forward 3.9940 | time_backward 6.0270 |
[2023-09-02 00:16:10,175::train::INFO] [train] Iter 03886 | loss 0.8749 | loss(rot) 0.2328 | loss(pos) 0.2550 | loss(seq) 0.3870 | grad 5.2729 | lr 0.0010 | time_forward 3.5120 | time_backward 4.9050 |
[2023-09-02 00:16:20,033::train::INFO] [train] Iter 03887 | loss 1.7955 | loss(rot) 0.6520 | loss(pos) 0.9049 | loss(seq) 0.2386 | grad 4.9441 | lr 0.0010 | time_forward 3.9830 | time_backward 5.8710 |
[2023-09-02 00:16:30,098::train::INFO] [train] Iter 03888 | loss 3.1327 | loss(rot) 2.3032 | loss(pos) 0.6880 | loss(seq) 0.1414 | grad 8.6053 | lr 0.0010 | time_forward 4.0090 | time_backward 6.0360 |
[2023-09-02 00:16:38,792::train::INFO] [train] Iter 03889 | loss 3.0297 | loss(rot) 2.5647 | loss(pos) 0.4607 | loss(seq) 0.0044 | grad 9.1554 | lr 0.0010 | time_forward 3.6200 | time_backward 5.0710 |
[2023-09-02 00:16:48,236::train::INFO] [train] Iter 03890 | loss 2.8719 | loss(rot) 2.6336 | loss(pos) 0.1783 | loss(seq) 0.0599 | grad 4.9261 | lr 0.0010 | time_forward 4.0960 | time_backward 5.3440 |
[2023-09-02 00:16:55,829::train::INFO] [train] Iter 03891 | loss 1.7966 | loss(rot) 1.2463 | loss(pos) 0.2638 | loss(seq) 0.2865 | grad 5.5151 | lr 0.0010 | time_forward 3.1890 | time_backward 4.4010 |
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