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[2023-10-24 19:53:12,700::train::INFO] [train] Iter 587165 | loss 0.5353 | loss(rot) 0.4471 | loss(pos) 0.0106 | loss(seq) 0.0777 | grad 2.7941 | lr 0.0000 | time_forward 1.4850 | time_backward 1.4400 |
[2023-10-24 19:53:20,883::train::INFO] [train] Iter 587166 | loss 0.5778 | loss(rot) 0.2684 | loss(pos) 0.1782 | loss(seq) 0.1313 | grad 4.0782 | lr 0.0000 | time_forward 3.6910 | time_backward 4.4880 |
[2023-10-24 19:53:28,966::train::INFO] [train] Iter 587167 | loss 0.4751 | loss(rot) 0.4435 | loss(pos) 0.0310 | loss(seq) 0.0006 | grad 4.0314 | lr 0.0000 | time_forward 3.4360 | time_backward 4.6430 |
[2023-10-24 19:53:37,081::train::INFO] [train] Iter 587168 | loss 0.6079 | loss(rot) 0.2276 | loss(pos) 0.0890 | loss(seq) 0.2913 | grad 4.3152 | lr 0.0000 | time_forward 3.4450 | time_backward 4.6660 |
[2023-10-24 19:53:46,080::train::INFO] [train] Iter 587169 | loss 0.2576 | loss(rot) 0.1360 | loss(pos) 0.0612 | loss(seq) 0.0604 | grad 2.9258 | lr 0.0000 | time_forward 3.6950 | time_backward 5.3000 |
[2023-10-24 19:53:56,647::train::INFO] [train] Iter 587170 | loss 1.1588 | loss(rot) 0.8613 | loss(pos) 0.1017 | loss(seq) 0.1957 | grad 5.1489 | lr 0.0000 | time_forward 3.8750 | time_backward 6.6890 |
[2023-10-24 19:53:59,565::train::INFO] [train] Iter 587171 | loss 0.6584 | loss(rot) 0.6383 | loss(pos) 0.0200 | loss(seq) 0.0001 | grad 3.3592 | lr 0.0000 | time_forward 1.4070 | time_backward 1.5080 |
[2023-10-24 19:54:08,803::train::INFO] [train] Iter 587172 | loss 0.3938 | loss(rot) 0.1156 | loss(pos) 0.0399 | loss(seq) 0.2383 | grad 2.0494 | lr 0.0000 | time_forward 3.7670 | time_backward 5.4400 |
[2023-10-24 19:54:17,145::train::INFO] [train] Iter 587173 | loss 0.1035 | loss(rot) 0.0368 | loss(pos) 0.0366 | loss(seq) 0.0300 | grad 1.9284 | lr 0.0000 | time_forward 3.6010 | time_backward 4.7360 |
[2023-10-24 19:54:25,939::train::INFO] [train] Iter 587174 | loss 0.4929 | loss(rot) 0.2062 | loss(pos) 0.0366 | loss(seq) 0.2501 | grad 1.9238 | lr 0.0000 | time_forward 3.5070 | time_backward 5.2830 |
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