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[2023-09-02 02:16:19,916::train::INFO] [train] Iter 04881 | loss 2.3415 | loss(rot) 2.1932 | loss(pos) 0.1367 | loss(seq) 0.0116 | grad 2.7755 | lr 0.0010 | time_forward 3.8940 | time_backward 5.4060 |
[2023-09-02 02:16:22,668::train::INFO] [train] Iter 04882 | loss 1.5127 | loss(rot) 1.3981 | loss(pos) 0.1143 | loss(seq) 0.0003 | grad 3.9505 | lr 0.0010 | time_forward 1.2670 | time_backward 1.4820 |
[2023-09-02 02:16:30,427::train::INFO] [train] Iter 04883 | loss 2.4826 | loss(rot) 0.0240 | loss(pos) 2.4586 | loss(seq) 0.0000 | grad 7.5238 | lr 0.0010 | time_forward 3.2590 | time_backward 4.4980 |
[2023-09-02 02:16:37,628::train::INFO] [train] Iter 04884 | loss 2.1953 | loss(rot) 1.6788 | loss(pos) 0.1616 | loss(seq) 0.3549 | grad 4.5572 | lr 0.0010 | time_forward 3.0390 | time_backward 4.1560 |
[2023-09-02 02:16:47,433::train::INFO] [train] Iter 04885 | loss 2.7441 | loss(rot) 2.3617 | loss(pos) 0.3824 | loss(seq) 0.0000 | grad 6.2904 | lr 0.0010 | time_forward 3.9030 | time_backward 5.8990 |
[2023-09-02 02:16:49,955::train::INFO] [train] Iter 04886 | loss 1.8737 | loss(rot) 1.6189 | loss(pos) 0.2502 | loss(seq) 0.0046 | grad 7.3716 | lr 0.0010 | time_forward 1.2370 | time_backward 1.2800 |
[2023-09-02 02:16:58,609::train::INFO] [train] Iter 04887 | loss 0.8384 | loss(rot) 0.1780 | loss(pos) 0.6479 | loss(seq) 0.0126 | grad 4.9730 | lr 0.0010 | time_forward 3.8510 | time_backward 4.7800 |
[2023-09-02 02:17:08,244::train::INFO] [train] Iter 04888 | loss 1.1930 | loss(rot) 0.1831 | loss(pos) 0.7739 | loss(seq) 0.2361 | grad 5.5863 | lr 0.0010 | time_forward 4.1400 | time_backward 5.4910 |
[2023-09-02 02:17:17,003::train::INFO] [train] Iter 04889 | loss 1.2049 | loss(rot) 0.3655 | loss(pos) 0.5431 | loss(seq) 0.2962 | grad 4.1324 | lr 0.0010 | time_forward 3.5870 | time_backward 5.1690 |
[2023-09-02 02:17:27,587::train::INFO] [train] Iter 04890 | loss 2.4119 | loss(rot) 1.6404 | loss(pos) 0.3277 | loss(seq) 0.4439 | grad 3.9916 | lr 0.0010 | time_forward 4.3400 | time_backward 6.2400 |
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