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[2023-09-02 07:33:50,258::train::INFO] [train] Iter 07478 | loss 1.9942 | loss(rot) 0.9211 | loss(pos) 0.4481 | loss(seq) 0.6250 | grad 5.1626 | lr 0.0010 | time_forward 4.1220 | time_backward 5.9410 |
[2023-09-02 07:33:52,923::train::INFO] [train] Iter 07479 | loss 1.9656 | loss(rot) 1.4960 | loss(pos) 0.0880 | loss(seq) 0.3815 | grad 4.7003 | lr 0.0010 | time_forward 1.2580 | time_backward 1.4040 |
[2023-09-02 07:33:57,686::train::INFO] [train] Iter 07480 | loss 2.3657 | loss(rot) 1.8172 | loss(pos) 0.1578 | loss(seq) 0.3906 | grad 4.5374 | lr 0.0010 | time_forward 2.1270 | time_backward 2.6330 |
[2023-09-02 07:34:00,221::train::INFO] [train] Iter 07481 | loss 2.7799 | loss(rot) 2.6624 | loss(pos) 0.1107 | loss(seq) 0.0068 | grad 5.4747 | lr 0.0010 | time_forward 1.1420 | time_backward 1.3890 |
[2023-09-02 07:34:09,092::train::INFO] [train] Iter 07482 | loss 2.8103 | loss(rot) 0.0647 | loss(pos) 2.7456 | loss(seq) 0.0000 | grad 9.1976 | lr 0.0010 | time_forward 3.9210 | time_backward 4.9470 |
[2023-09-02 07:34:17,970::train::INFO] [train] Iter 07483 | loss 1.2401 | loss(rot) 0.2116 | loss(pos) 0.9220 | loss(seq) 0.1065 | grad 3.9618 | lr 0.0010 | time_forward 3.8840 | time_backward 4.9910 |
[2023-09-02 07:34:20,733::train::INFO] [train] Iter 07484 | loss 1.8699 | loss(rot) 1.3278 | loss(pos) 0.1387 | loss(seq) 0.4034 | grad 5.8257 | lr 0.0010 | time_forward 1.3410 | time_backward 1.4190 |
[2023-09-02 07:34:29,507::train::INFO] [train] Iter 07485 | loss 1.6910 | loss(rot) 1.5901 | loss(pos) 0.1003 | loss(seq) 0.0006 | grad 8.3404 | lr 0.0010 | time_forward 3.7370 | time_backward 5.0340 |
[2023-09-02 07:34:31,756::train::INFO] [train] Iter 07486 | loss 1.5033 | loss(rot) 1.2318 | loss(pos) 0.1134 | loss(seq) 0.1582 | grad 5.0748 | lr 0.0010 | time_forward 1.0630 | time_backward 1.1830 |
[2023-09-02 07:34:34,166::train::INFO] [train] Iter 07487 | loss 2.2797 | loss(rot) 2.1922 | loss(pos) 0.0808 | loss(seq) 0.0068 | grad 4.1374 | lr 0.0010 | time_forward 1.1770 | time_backward 1.1990 |
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