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[2023-09-02 05:08:28,136::train::INFO] [train] Iter 06279 | loss 1.1869 | loss(rot) 0.5159 | loss(pos) 0.2349 | loss(seq) 0.4361 | grad 4.0755 | lr 0.0010 | time_forward 3.9030 | time_backward 5.3380 |
[2023-09-02 05:08:30,792::train::INFO] [train] Iter 06280 | loss 2.8268 | loss(rot) 0.0356 | loss(pos) 2.7905 | loss(seq) 0.0006 | grad 5.8989 | lr 0.0010 | time_forward 1.2440 | time_backward 1.4090 |
[2023-09-02 05:08:38,984::train::INFO] [train] Iter 06281 | loss 2.0686 | loss(rot) 1.4692 | loss(pos) 0.2299 | loss(seq) 0.3695 | grad 5.8281 | lr 0.0010 | time_forward 3.5120 | time_backward 4.6770 |
[2023-09-02 05:08:47,508::train::INFO] [train] Iter 06282 | loss 2.5433 | loss(rot) 2.2886 | loss(pos) 0.2546 | loss(seq) 0.0000 | grad 4.3651 | lr 0.0010 | time_forward 3.6810 | time_backward 4.8390 |
[2023-09-02 05:08:56,555::train::INFO] [train] Iter 06283 | loss 1.1029 | loss(rot) 0.0680 | loss(pos) 1.0203 | loss(seq) 0.0146 | grad 4.7583 | lr 0.0010 | time_forward 3.8140 | time_backward 5.2300 |
[2023-09-02 05:09:06,674::train::INFO] [train] Iter 06284 | loss 2.0870 | loss(rot) 1.3021 | loss(pos) 0.3263 | loss(seq) 0.4586 | grad 4.7212 | lr 0.0010 | time_forward 4.0380 | time_backward 6.0790 |
[2023-09-02 05:09:16,651::train::INFO] [train] Iter 06285 | loss 3.3986 | loss(rot) 0.0076 | loss(pos) 3.3904 | loss(seq) 0.0006 | grad 7.0132 | lr 0.0010 | time_forward 4.0720 | time_backward 5.9000 |
[2023-09-02 05:09:19,279::train::INFO] [train] Iter 06286 | loss 2.0783 | loss(rot) 1.7960 | loss(pos) 0.2305 | loss(seq) 0.0519 | grad 3.9657 | lr 0.0010 | time_forward 1.2360 | time_backward 1.3890 |
[2023-09-02 05:09:28,202::train::INFO] [train] Iter 06287 | loss 2.1250 | loss(rot) 1.7984 | loss(pos) 0.1668 | loss(seq) 0.1598 | grad 3.8337 | lr 0.0010 | time_forward 3.7500 | time_backward 5.1690 |
[2023-09-02 05:09:30,869::train::INFO] [train] Iter 06288 | loss 1.5944 | loss(rot) 1.0112 | loss(pos) 0.2856 | loss(seq) 0.2975 | grad 4.3468 | lr 0.0010 | time_forward 1.2430 | time_backward 1.4220 |
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