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[2023-10-22 21:52:54,047::train::INFO] [train] Iter 562390 | loss 0.1663 | loss(rot) 0.0265 | loss(pos) 0.1327 | loss(seq) 0.0071 | grad 3.7594 | lr 0.0000 | time_forward 3.3840 | time_backward 4.4930 |
[2023-10-22 21:52:56,857::train::INFO] [train] Iter 562391 | loss 0.2137 | loss(rot) 0.1390 | loss(pos) 0.0346 | loss(seq) 0.0401 | grad 2.1099 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4990 |
[2023-10-22 21:52:59,596::train::INFO] [train] Iter 562392 | loss 0.3767 | loss(rot) 0.1986 | loss(pos) 0.0127 | loss(seq) 0.1654 | grad 2.3747 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4030 |
[2023-10-22 21:53:06,408::train::INFO] [train] Iter 562393 | loss 0.2740 | loss(rot) 0.0796 | loss(pos) 0.1004 | loss(seq) 0.0940 | grad 3.0368 | lr 0.0000 | time_forward 2.9770 | time_backward 3.8330 |
[2023-10-22 21:53:13,936::train::INFO] [train] Iter 562394 | loss 2.3450 | loss(rot) 1.1234 | loss(pos) 0.7345 | loss(seq) 0.4871 | grad 9.0150 | lr 0.0000 | time_forward 3.2290 | time_backward 4.2950 |
[2023-10-22 21:53:16,653::train::INFO] [train] Iter 562395 | loss 1.2303 | loss(rot) 0.6933 | loss(pos) 0.1148 | loss(seq) 0.4222 | grad 10.3545 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4170 |
[2023-10-22 21:53:18,909::train::INFO] [train] Iter 562396 | loss 1.2832 | loss(rot) 0.9668 | loss(pos) 0.0660 | loss(seq) 0.2505 | grad 4.0586 | lr 0.0000 | time_forward 1.0570 | time_backward 1.1960 |
[2023-10-22 21:53:21,604::train::INFO] [train] Iter 562397 | loss 0.2913 | loss(rot) 0.1238 | loss(pos) 0.0249 | loss(seq) 0.1426 | grad 1.8996 | lr 0.0000 | time_forward 1.2900 | time_backward 1.3950 |
[2023-10-22 21:53:29,744::train::INFO] [train] Iter 562398 | loss 0.2292 | loss(rot) 0.1509 | loss(pos) 0.0429 | loss(seq) 0.0354 | grad 3.3957 | lr 0.0000 | time_forward 3.5040 | time_backward 4.6320 |
[2023-10-22 21:53:38,647::train::INFO] [train] Iter 562399 | loss 0.9094 | loss(rot) 0.4566 | loss(pos) 0.1409 | loss(seq) 0.3119 | grad 3.7407 | lr 0.0000 | time_forward 3.7650 | time_backward 5.1340 |
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