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[2023-09-02 20:42:54,491::train::INFO] [train] Iter 14071 | loss 0.8891 | loss(rot) 0.7480 | loss(pos) 0.1391 | loss(seq) 0.0021 | grad 5.2802 | lr 0.0010 | time_forward 3.8260 | time_backward 5.1770 |
[2023-09-02 20:43:05,869::train::INFO] [train] Iter 14072 | loss 2.6063 | loss(rot) 2.1968 | loss(pos) 0.3840 | loss(seq) 0.0255 | grad 5.9208 | lr 0.0010 | time_forward 4.1810 | time_backward 7.1910 |
[2023-09-02 20:43:08,710::train::INFO] [train] Iter 14073 | loss 0.8997 | loss(rot) 0.4061 | loss(pos) 0.2427 | loss(seq) 0.2509 | grad 5.1719 | lr 0.0010 | time_forward 1.3210 | time_backward 1.4970 |
[2023-09-02 20:43:19,587::train::INFO] [train] Iter 14074 | loss 0.8439 | loss(rot) 0.2242 | loss(pos) 0.3281 | loss(seq) 0.2917 | grad 3.2895 | lr 0.0010 | time_forward 4.3770 | time_backward 6.4590 |
[2023-09-02 20:43:27,405::train::INFO] [train] Iter 14075 | loss 1.8412 | loss(rot) 1.2558 | loss(pos) 0.1675 | loss(seq) 0.4179 | grad 4.0946 | lr 0.0010 | time_forward 3.3300 | time_backward 4.4850 |
[2023-09-02 20:43:38,086::train::INFO] [train] Iter 14076 | loss 2.4006 | loss(rot) 1.8852 | loss(pos) 0.2042 | loss(seq) 0.3112 | grad 4.2679 | lr 0.0010 | time_forward 4.4500 | time_backward 6.2280 |
[2023-09-02 20:43:47,297::train::INFO] [train] Iter 14077 | loss 1.4807 | loss(rot) 1.2418 | loss(pos) 0.2389 | loss(seq) 0.0000 | grad 6.0102 | lr 0.0010 | time_forward 3.8910 | time_backward 5.3170 |
[2023-09-02 20:43:52,137::train::INFO] [train] Iter 14078 | loss 2.0425 | loss(rot) 1.9038 | loss(pos) 0.1358 | loss(seq) 0.0029 | grad 5.4245 | lr 0.0010 | time_forward 2.1680 | time_backward 2.6680 |
[2023-09-02 20:44:01,531::train::INFO] [train] Iter 14079 | loss 0.6518 | loss(rot) 0.4762 | loss(pos) 0.0498 | loss(seq) 0.1257 | grad 3.4583 | lr 0.0010 | time_forward 3.8770 | time_backward 5.5130 |
[2023-09-02 20:44:10,228::train::INFO] [train] Iter 14080 | loss 1.1696 | loss(rot) 0.1576 | loss(pos) 0.9595 | loss(seq) 0.0524 | grad 3.1303 | lr 0.0010 | time_forward 3.5340 | time_backward 5.1590 |
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