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[2023-09-02 13:51:07,332::train::INFO] [train] Iter 10576 | loss 1.7075 | loss(rot) 0.9265 | loss(pos) 0.2798 | loss(seq) 0.5012 | grad 5.3225 | lr 0.0010 | time_forward 3.9640 | time_backward 5.7670 |
[2023-09-02 13:51:16,427::train::INFO] [train] Iter 10577 | loss 2.8809 | loss(rot) 2.4709 | loss(pos) 0.1847 | loss(seq) 0.2252 | grad 4.7222 | lr 0.0010 | time_forward 3.7170 | time_backward 5.3740 |
[2023-09-02 13:51:19,139::train::INFO] [train] Iter 10578 | loss 1.8831 | loss(rot) 1.0615 | loss(pos) 0.4622 | loss(seq) 0.3594 | grad 5.0737 | lr 0.0010 | time_forward 1.2570 | time_backward 1.4510 |
[2023-09-02 13:51:21,462::train::INFO] [train] Iter 10579 | loss 2.1665 | loss(rot) 1.9885 | loss(pos) 0.1777 | loss(seq) 0.0003 | grad 4.0047 | lr 0.0010 | time_forward 1.0970 | time_backward 1.2190 |
[2023-09-02 13:51:24,129::train::INFO] [train] Iter 10580 | loss 2.2048 | loss(rot) 1.8089 | loss(pos) 0.1265 | loss(seq) 0.2693 | grad 4.4885 | lr 0.0010 | time_forward 1.2360 | time_backward 1.4250 |
[2023-09-02 13:51:26,450::train::INFO] [train] Iter 10581 | loss 0.9574 | loss(rot) 0.5048 | loss(pos) 0.3510 | loss(seq) 0.1016 | grad 3.4578 | lr 0.0010 | time_forward 1.0920 | time_backward 1.2230 |
[2023-09-02 13:51:34,058::train::INFO] [train] Iter 10582 | loss 2.2394 | loss(rot) 2.0234 | loss(pos) 0.1016 | loss(seq) 0.1145 | grad 5.0816 | lr 0.0010 | time_forward 3.2780 | time_backward 4.3230 |
[2023-09-02 13:51:44,713::train::INFO] [train] Iter 10583 | loss 2.5456 | loss(rot) 1.4989 | loss(pos) 0.3314 | loss(seq) 0.7152 | grad 4.3968 | lr 0.0010 | time_forward 4.3310 | time_backward 6.3210 |
[2023-09-02 13:51:47,489::train::INFO] [train] Iter 10584 | loss 1.5273 | loss(rot) 1.1467 | loss(pos) 0.0832 | loss(seq) 0.2974 | grad 4.4505 | lr 0.0010 | time_forward 1.3140 | time_backward 1.4580 |
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