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[2023-09-02 04:06:34,326::train::INFO] [train] Iter 05780 | loss 2.1099 | loss(rot) 1.9857 | loss(pos) 0.0569 | loss(seq) 0.0673 | grad 2.8161 | lr 0.0010 | time_forward 3.5650 | time_backward 4.7650 |
[2023-09-02 04:06:36,958::train::INFO] [train] Iter 05781 | loss 3.0238 | loss(rot) 0.0063 | loss(pos) 3.0176 | loss(seq) 0.0000 | grad 6.7120 | lr 0.0010 | time_forward 1.2460 | time_backward 1.3820 |
[2023-09-02 04:06:45,847::train::INFO] [train] Iter 05782 | loss 0.7288 | loss(rot) 0.2226 | loss(pos) 0.4601 | loss(seq) 0.0461 | grad 2.7375 | lr 0.0010 | time_forward 3.6360 | time_backward 5.2260 |
[2023-09-02 04:06:55,197::train::INFO] [train] Iter 05783 | loss 1.4345 | loss(rot) 0.0365 | loss(pos) 1.3930 | loss(seq) 0.0050 | grad 6.1155 | lr 0.0010 | time_forward 3.9910 | time_backward 5.3570 |
[2023-09-02 04:07:02,418::train::INFO] [train] Iter 05784 | loss 2.8347 | loss(rot) 2.0409 | loss(pos) 0.3604 | loss(seq) 0.4334 | grad 3.6067 | lr 0.0010 | time_forward 3.0270 | time_backward 4.1910 |
[2023-09-02 04:07:09,511::train::INFO] [train] Iter 05785 | loss 1.6776 | loss(rot) 1.0731 | loss(pos) 0.1514 | loss(seq) 0.4532 | grad 4.8729 | lr 0.0010 | time_forward 3.0100 | time_backward 4.0790 |
[2023-09-02 04:07:12,256::train::INFO] [train] Iter 05786 | loss 1.9397 | loss(rot) 1.4158 | loss(pos) 0.1487 | loss(seq) 0.3753 | grad 4.8036 | lr 0.0010 | time_forward 1.3080 | time_backward 1.4340 |
[2023-09-02 04:07:21,465::train::INFO] [train] Iter 05787 | loss 2.2108 | loss(rot) 1.1531 | loss(pos) 0.4169 | loss(seq) 0.6408 | grad 5.0376 | lr 0.0010 | time_forward 3.9310 | time_backward 5.2740 |
[2023-09-02 04:07:30,759::train::INFO] [train] Iter 05788 | loss 2.1936 | loss(rot) 1.8220 | loss(pos) 0.1274 | loss(seq) 0.2443 | grad 2.8864 | lr 0.0010 | time_forward 3.8060 | time_backward 5.4850 |
[2023-09-02 04:07:40,654::train::INFO] [train] Iter 05789 | loss 1.6154 | loss(rot) 0.1404 | loss(pos) 1.4596 | loss(seq) 0.0154 | grad 5.0526 | lr 0.0010 | time_forward 4.0940 | time_backward 5.7980 |
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