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[2023-10-23 22:00:22,044::train::INFO] [train] Iter 575877 | loss 0.7589 | loss(rot) 0.5884 | loss(pos) 0.0207 | loss(seq) 0.1498 | grad 3.2167 | lr 0.0000 | time_forward 4.0990 | time_backward 5.8850 |
[2023-10-23 22:00:32,052::train::INFO] [train] Iter 575878 | loss 0.5355 | loss(rot) 0.2963 | loss(pos) 0.0366 | loss(seq) 0.2026 | grad 1.8081 | lr 0.0000 | time_forward 4.0540 | time_backward 5.9500 |
[2023-10-23 22:00:34,822::train::INFO] [train] Iter 575879 | loss 0.7293 | loss(rot) 0.6984 | loss(pos) 0.0309 | loss(seq) 0.0000 | grad 19.6818 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4320 |
[2023-10-23 22:00:42,815::train::INFO] [train] Iter 575880 | loss 0.4646 | loss(rot) 0.2510 | loss(pos) 0.0266 | loss(seq) 0.1870 | grad 4.4624 | lr 0.0000 | time_forward 3.4620 | time_backward 4.5280 |
[2023-10-23 22:00:51,330::train::INFO] [train] Iter 575881 | loss 0.1481 | loss(rot) 0.1218 | loss(pos) 0.0250 | loss(seq) 0.0013 | grad 2.8192 | lr 0.0000 | time_forward 3.6240 | time_backward 4.8880 |
[2023-10-23 22:00:59,446::train::INFO] [train] Iter 575882 | loss 2.0464 | loss(rot) 1.9635 | loss(pos) 0.0698 | loss(seq) 0.0132 | grad 4.5205 | lr 0.0000 | time_forward 3.4230 | time_backward 4.6900 |
[2023-10-23 22:01:02,663::train::INFO] [train] Iter 575883 | loss 0.8587 | loss(rot) 0.3102 | loss(pos) 0.1602 | loss(seq) 0.3883 | grad 2.1928 | lr 0.0000 | time_forward 1.4440 | time_backward 1.7700 |
[2023-10-23 22:01:11,537::train::INFO] [train] Iter 575884 | loss 0.1201 | loss(rot) 0.0351 | loss(pos) 0.0573 | loss(seq) 0.0278 | grad 1.9513 | lr 0.0000 | time_forward 3.7680 | time_backward 5.0920 |
[2023-10-23 22:01:19,023::train::INFO] [train] Iter 575885 | loss 0.0701 | loss(rot) 0.0484 | loss(pos) 0.0135 | loss(seq) 0.0082 | grad 2.0022 | lr 0.0000 | time_forward 3.1020 | time_backward 4.3800 |
[2023-10-23 22:01:25,924::train::INFO] [train] Iter 575886 | loss 0.3610 | loss(rot) 0.1022 | loss(pos) 0.0549 | loss(seq) 0.2038 | grad 3.2675 | lr 0.0000 | time_forward 2.9490 | time_backward 3.9490 |
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