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[2023-09-02 18:40:42,369::train::INFO] [train] Iter 13073 | loss 1.4225 | loss(rot) 0.6974 | loss(pos) 0.1675 | loss(seq) 0.5576 | grad 3.4501 | lr 0.0010 | time_forward 3.7530 | time_backward 5.2110 |
[2023-09-02 18:40:51,915::train::INFO] [train] Iter 13074 | loss 1.3909 | loss(rot) 1.2652 | loss(pos) 0.1251 | loss(seq) 0.0006 | grad 6.3137 | lr 0.0010 | time_forward 4.0510 | time_backward 5.4930 |
[2023-09-02 18:40:54,647::train::INFO] [train] Iter 13075 | loss 0.9829 | loss(rot) 0.0507 | loss(pos) 0.9165 | loss(seq) 0.0157 | grad 4.5990 | lr 0.0010 | time_forward 1.2710 | time_backward 1.4580 |
[2023-09-02 18:40:57,338::train::INFO] [train] Iter 13076 | loss 1.4504 | loss(rot) 0.5186 | loss(pos) 0.3245 | loss(seq) 0.6074 | grad 4.2809 | lr 0.0010 | time_forward 1.2660 | time_backward 1.4220 |
[2023-09-02 18:41:05,785::train::INFO] [train] Iter 13077 | loss 1.4914 | loss(rot) 1.3038 | loss(pos) 0.1802 | loss(seq) 0.0074 | grad 3.9605 | lr 0.0010 | time_forward 3.5740 | time_backward 4.8700 |
[2023-09-02 18:41:08,446::train::INFO] [train] Iter 13078 | loss 1.2281 | loss(rot) 1.1474 | loss(pos) 0.0807 | loss(seq) 0.0000 | grad 7.2363 | lr 0.0010 | time_forward 1.2500 | time_backward 1.4070 |
[2023-09-02 18:41:17,150::train::INFO] [train] Iter 13079 | loss 0.6006 | loss(rot) 0.2804 | loss(pos) 0.2608 | loss(seq) 0.0594 | grad 2.9800 | lr 0.0010 | time_forward 3.6740 | time_backward 5.0280 |
[2023-09-02 18:41:25,144::train::INFO] [train] Iter 13080 | loss 1.8276 | loss(rot) 1.5580 | loss(pos) 0.2530 | loss(seq) 0.0165 | grad 6.8252 | lr 0.0010 | time_forward 3.3840 | time_backward 4.6060 |
[2023-09-02 18:41:33,312::train::INFO] [train] Iter 13081 | loss 1.6695 | loss(rot) 1.6001 | loss(pos) 0.0674 | loss(seq) 0.0020 | grad 5.5443 | lr 0.0010 | time_forward 3.4190 | time_backward 4.7470 |
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