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[2023-09-02 01:18:53,437::train::INFO] [train] Iter 04381 | loss 3.9596 | loss(rot) 0.0262 | loss(pos) 3.9334 | loss(seq) 0.0000 | grad 6.8281 | lr 0.0010 | time_forward 4.1280 | time_backward 5.8730 |
[2023-09-02 01:19:03,481::train::INFO] [train] Iter 04382 | loss 0.9316 | loss(rot) 0.1099 | loss(pos) 0.5398 | loss(seq) 0.2819 | grad 3.3008 | lr 0.0010 | time_forward 3.9880 | time_backward 6.0520 |
[2023-09-02 01:19:13,697::train::INFO] [train] Iter 04383 | loss 2.8054 | loss(rot) 2.3763 | loss(pos) 0.1879 | loss(seq) 0.2413 | grad 3.5192 | lr 0.0010 | time_forward 4.1200 | time_backward 6.0930 |
[2023-09-02 01:19:23,762::train::INFO] [train] Iter 04384 | loss 2.1642 | loss(rot) 1.3137 | loss(pos) 0.2626 | loss(seq) 0.5878 | grad 3.7772 | lr 0.0010 | time_forward 4.0450 | time_backward 6.0160 |
[2023-09-02 01:19:26,518::train::INFO] [train] Iter 04385 | loss 3.0911 | loss(rot) 2.9752 | loss(pos) 0.1158 | loss(seq) 0.0000 | grad 3.8570 | lr 0.0010 | time_forward 1.2740 | time_backward 1.4790 |
[2023-09-02 01:19:29,320::train::INFO] [train] Iter 04386 | loss 2.7298 | loss(rot) 2.5341 | loss(pos) 0.1938 | loss(seq) 0.0018 | grad 3.1146 | lr 0.0010 | time_forward 1.3230 | time_backward 1.4760 |
[2023-09-02 01:19:32,184::train::INFO] [train] Iter 04387 | loss 2.4592 | loss(rot) 1.6469 | loss(pos) 0.2979 | loss(seq) 0.5145 | grad 6.3923 | lr 0.0010 | time_forward 1.3650 | time_backward 1.4950 |
[2023-09-02 01:19:35,690::train::INFO] [train] Iter 04388 | loss 2.1076 | loss(rot) 0.3360 | loss(pos) 1.7385 | loss(seq) 0.0330 | grad 6.8425 | lr 0.0010 | time_forward 1.5180 | time_backward 1.9590 |
[2023-09-02 01:19:45,882::train::INFO] [train] Iter 04389 | loss 2.5061 | loss(rot) 1.6363 | loss(pos) 0.2336 | loss(seq) 0.6362 | grad 4.2778 | lr 0.0010 | time_forward 4.2540 | time_backward 5.9220 |
[2023-09-02 01:19:55,206::train::INFO] [train] Iter 04390 | loss 0.7954 | loss(rot) 0.0682 | loss(pos) 0.7173 | loss(seq) 0.0099 | grad 4.2147 | lr 0.0010 | time_forward 3.9170 | time_backward 5.4040 |
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