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[2023-09-02 12:03:31,883::train::INFO] [train] Iter 09676 | loss 1.5011 | loss(rot) 0.2910 | loss(pos) 0.7761 | loss(seq) 0.4340 | grad 6.5908 | lr 0.0010 | time_forward 4.2800 | time_backward 6.2140 |
[2023-09-02 12:03:40,832::train::INFO] [train] Iter 09677 | loss 0.7473 | loss(rot) 0.0906 | loss(pos) 0.6255 | loss(seq) 0.0312 | grad 5.3746 | lr 0.0010 | time_forward 3.9160 | time_backward 5.0290 |
[2023-09-02 12:03:50,544::train::INFO] [train] Iter 09678 | loss 2.3024 | loss(rot) 1.3349 | loss(pos) 0.3760 | loss(seq) 0.5915 | grad 4.3818 | lr 0.0010 | time_forward 4.1220 | time_backward 5.5870 |
[2023-09-02 12:03:59,010::train::INFO] [train] Iter 09679 | loss 0.6649 | loss(rot) 0.0701 | loss(pos) 0.5748 | loss(seq) 0.0200 | grad 3.7284 | lr 0.0010 | time_forward 3.5920 | time_backward 4.8700 |
[2023-09-02 12:04:08,079::train::INFO] [train] Iter 09680 | loss 1.5530 | loss(rot) 0.6746 | loss(pos) 0.2120 | loss(seq) 0.6664 | grad 4.5767 | lr 0.0010 | time_forward 3.8840 | time_backward 5.1820 |
[2023-09-02 12:04:10,750::train::INFO] [train] Iter 09681 | loss 2.5995 | loss(rot) 1.9861 | loss(pos) 0.1908 | loss(seq) 0.4226 | grad 3.4655 | lr 0.0010 | time_forward 1.2480 | time_backward 1.4190 |
[2023-09-02 12:04:19,517::train::INFO] [train] Iter 09682 | loss 0.5800 | loss(rot) 0.1908 | loss(pos) 0.3635 | loss(seq) 0.0257 | grad 3.0858 | lr 0.0010 | time_forward 3.7080 | time_backward 5.0550 |
[2023-09-02 12:04:28,644::train::INFO] [train] Iter 09683 | loss 1.1439 | loss(rot) 0.7923 | loss(pos) 0.0808 | loss(seq) 0.2708 | grad 2.8895 | lr 0.0010 | time_forward 3.8730 | time_backward 5.2500 |
[2023-09-02 12:04:31,957::train::INFO] [train] Iter 09684 | loss 2.0855 | loss(rot) 1.9159 | loss(pos) 0.1271 | loss(seq) 0.0426 | grad 3.1871 | lr 0.0010 | time_forward 1.4520 | time_backward 1.8590 |
[2023-09-02 12:04:40,053::train::INFO] [train] Iter 09685 | loss 1.7085 | loss(rot) 1.5701 | loss(pos) 0.0867 | loss(seq) 0.0517 | grad 6.3163 | lr 0.0010 | time_forward 3.4610 | time_backward 4.6310 |
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