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[2023-09-02 23:42:09,573::train::INFO] [train] Iter 15470 | loss 1.5489 | loss(rot) 1.3174 | loss(pos) 0.1879 | loss(seq) 0.0436 | grad 3.6935 | lr 0.0010 | time_forward 4.0870 | time_backward 5.7310 |
[2023-09-02 23:42:14,672::train::INFO] [train] Iter 15471 | loss 0.8815 | loss(rot) 0.3732 | loss(pos) 0.0923 | loss(seq) 0.4160 | grad 4.0705 | lr 0.0010 | time_forward 2.2200 | time_backward 2.8740 |
[2023-09-02 23:42:24,266::train::INFO] [train] Iter 15472 | loss 1.0414 | loss(rot) 0.0720 | loss(pos) 0.9422 | loss(seq) 0.0271 | grad 5.0442 | lr 0.0010 | time_forward 3.8650 | time_backward 5.7230 |
[2023-09-02 23:42:27,276::train::INFO] [train] Iter 15473 | loss 1.1128 | loss(rot) 0.3325 | loss(pos) 0.7518 | loss(seq) 0.0286 | grad 4.1648 | lr 0.0010 | time_forward 1.4350 | time_backward 1.5080 |
[2023-09-02 23:42:30,235::train::INFO] [train] Iter 15474 | loss 1.0929 | loss(rot) 0.5015 | loss(pos) 0.1861 | loss(seq) 0.4053 | grad 1.9757 | lr 0.0010 | time_forward 1.4340 | time_backward 1.5190 |
[2023-09-02 23:42:39,817::train::INFO] [train] Iter 15475 | loss 0.8806 | loss(rot) 0.5694 | loss(pos) 0.2488 | loss(seq) 0.0624 | grad 5.3390 | lr 0.0010 | time_forward 4.0970 | time_backward 5.4600 |
[2023-09-02 23:42:42,762::train::INFO] [train] Iter 15476 | loss 0.9835 | loss(rot) 0.4122 | loss(pos) 0.4826 | loss(seq) 0.0886 | grad 5.0700 | lr 0.0010 | time_forward 1.3630 | time_backward 1.5780 |
[2023-09-02 23:42:52,388::train::INFO] [train] Iter 15477 | loss 1.2331 | loss(rot) 0.2299 | loss(pos) 0.6623 | loss(seq) 0.3408 | grad 5.9698 | lr 0.0010 | time_forward 4.0420 | time_backward 5.5800 |
[2023-09-02 23:43:03,155::train::INFO] [train] Iter 15478 | loss 1.1506 | loss(rot) 0.5073 | loss(pos) 0.1846 | loss(seq) 0.4588 | grad 3.1012 | lr 0.0010 | time_forward 4.2610 | time_backward 6.5020 |
[2023-09-02 23:43:12,278::train::INFO] [train] Iter 15479 | loss 1.2077 | loss(rot) 0.0190 | loss(pos) 1.1840 | loss(seq) 0.0047 | grad 5.9438 | lr 0.0010 | time_forward 3.8020 | time_backward 5.3160 |
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