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[2023-09-02 11:38:31,950::train::INFO] [train] Iter 09476 | loss 1.6300 | loss(rot) 0.5765 | loss(pos) 0.6928 | loss(seq) 0.3608 | grad 3.0271 | lr 0.0010 | time_forward 4.0630 | time_backward 5.9830 |
[2023-09-02 11:38:41,779::train::INFO] [train] Iter 09477 | loss 1.2153 | loss(rot) 0.2236 | loss(pos) 0.7459 | loss(seq) 0.2459 | grad 4.1923 | lr 0.0010 | time_forward 4.0440 | time_backward 5.7820 |
[2023-09-02 11:38:50,285::train::INFO] [train] Iter 09478 | loss 2.6414 | loss(rot) 2.2507 | loss(pos) 0.1913 | loss(seq) 0.1994 | grad 6.4306 | lr 0.0010 | time_forward 3.5590 | time_backward 4.9430 |
[2023-09-02 11:38:53,025::train::INFO] [train] Iter 09479 | loss 2.2324 | loss(rot) 2.1063 | loss(pos) 0.1009 | loss(seq) 0.0252 | grad 5.0510 | lr 0.0010 | time_forward 1.3130 | time_backward 1.4240 |
[2023-09-02 11:38:55,739::train::INFO] [train] Iter 09480 | loss 2.3677 | loss(rot) 1.7091 | loss(pos) 0.2276 | loss(seq) 0.4311 | grad 6.9063 | lr 0.0010 | time_forward 1.2850 | time_backward 1.4250 |
[2023-09-02 11:39:05,709::train::INFO] [train] Iter 09481 | loss 2.2505 | loss(rot) 1.7988 | loss(pos) 0.4514 | loss(seq) 0.0003 | grad 4.1862 | lr 0.0010 | time_forward 4.0780 | time_backward 5.8880 |
[2023-09-02 11:39:08,464::train::INFO] [train] Iter 09482 | loss 2.3889 | loss(rot) 2.1203 | loss(pos) 0.2680 | loss(seq) 0.0006 | grad 7.3259 | lr 0.0010 | time_forward 1.3440 | time_backward 1.4070 |
[2023-09-02 11:39:16,825::train::INFO] [train] Iter 09483 | loss 1.1496 | loss(rot) 0.6804 | loss(pos) 0.3394 | loss(seq) 0.1299 | grad 4.4340 | lr 0.0010 | time_forward 3.7400 | time_backward 4.6180 |
[2023-09-02 11:39:24,874::train::INFO] [train] Iter 09484 | loss 3.3352 | loss(rot) 0.1535 | loss(pos) 3.1812 | loss(seq) 0.0005 | grad 8.6670 | lr 0.0010 | time_forward 3.4230 | time_backward 4.6230 |
[2023-09-02 11:39:27,604::train::INFO] [train] Iter 09485 | loss 3.1014 | loss(rot) 1.9556 | loss(pos) 0.6661 | loss(seq) 0.4797 | grad 20.0078 | lr 0.0010 | time_forward 1.2860 | time_backward 1.4400 |
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