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[2023-09-02 12:27:28,674::train::INFO] [train] Iter 09876 | loss 1.7031 | loss(rot) 1.6186 | loss(pos) 0.0833 | loss(seq) 0.0012 | grad 4.2481 | lr 0.0010 | time_forward 3.4480 | time_backward 4.8400 |
[2023-09-02 12:27:31,338::train::INFO] [train] Iter 09877 | loss 2.5884 | loss(rot) 1.8420 | loss(pos) 0.2464 | loss(seq) 0.4999 | grad 3.3797 | lr 0.0010 | time_forward 1.2230 | time_backward 1.4380 |
[2023-09-02 12:27:39,253::train::INFO] [train] Iter 09878 | loss 2.4834 | loss(rot) 2.2612 | loss(pos) 0.1286 | loss(seq) 0.0936 | grad 5.6300 | lr 0.0010 | time_forward 3.2400 | time_backward 4.6720 |
[2023-09-02 12:27:49,253::train::INFO] [train] Iter 09879 | loss 1.1929 | loss(rot) 0.3031 | loss(pos) 0.3535 | loss(seq) 0.5363 | grad 2.9819 | lr 0.0010 | time_forward 4.1900 | time_backward 5.8060 |
[2023-09-02 12:27:52,531::train::INFO] [train] Iter 09880 | loss 1.1466 | loss(rot) 0.3085 | loss(pos) 0.6573 | loss(seq) 0.1808 | grad 2.7514 | lr 0.0010 | time_forward 1.3850 | time_backward 1.8890 |
[2023-09-02 12:28:02,445::train::INFO] [train] Iter 09881 | loss 1.4636 | loss(rot) 0.7963 | loss(pos) 0.1906 | loss(seq) 0.4767 | grad 4.4314 | lr 0.0010 | time_forward 4.0100 | time_backward 5.9000 |
[2023-09-02 12:28:04,886::train::INFO] [train] Iter 09882 | loss 1.4821 | loss(rot) 1.3744 | loss(pos) 0.0982 | loss(seq) 0.0095 | grad 2.8312 | lr 0.0010 | time_forward 1.1390 | time_backward 1.2980 |
[2023-09-02 12:28:07,559::train::INFO] [train] Iter 09883 | loss 1.2061 | loss(rot) 1.0374 | loss(pos) 0.1686 | loss(seq) 0.0001 | grad 4.9638 | lr 0.0010 | time_forward 1.2500 | time_backward 1.4190 |
[2023-09-02 12:28:14,993::train::INFO] [train] Iter 09884 | loss 1.9109 | loss(rot) 0.8671 | loss(pos) 0.6380 | loss(seq) 0.4058 | grad 6.2843 | lr 0.0010 | time_forward 3.1440 | time_backward 4.2860 |
[2023-09-02 12:28:23,053::train::INFO] [train] Iter 09885 | loss 2.3713 | loss(rot) 1.7339 | loss(pos) 0.2395 | loss(seq) 0.3979 | grad 6.7788 | lr 0.0010 | time_forward 3.3410 | time_backward 4.7160 |
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