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[2023-10-25 19:50:50,551::train::INFO] [train] Iter 599653 | loss 0.3081 | loss(rot) 0.1330 | loss(pos) 0.0178 | loss(seq) 0.1573 | grad 2.5006 | lr 0.0000 | time_forward 1.1680 | time_backward 1.3500 |
[2023-10-25 19:50:52,977::train::INFO] [train] Iter 599654 | loss 1.2461 | loss(rot) 0.7629 | loss(pos) 0.0590 | loss(seq) 0.4243 | grad 4.8815 | lr 0.0000 | time_forward 1.1110 | time_backward 1.3110 |
[2023-10-25 19:51:03,198::train::INFO] [train] Iter 599655 | loss 1.0023 | loss(rot) 0.8503 | loss(pos) 0.0410 | loss(seq) 0.1109 | grad 2.8209 | lr 0.0000 | time_forward 4.1620 | time_backward 6.0520 |
[2023-10-25 19:51:13,463::train::INFO] [train] Iter 599656 | loss 0.8492 | loss(rot) 0.2090 | loss(pos) 0.2032 | loss(seq) 0.4371 | grad 3.1471 | lr 0.0000 | time_forward 4.1450 | time_backward 6.1170 |
[2023-10-25 19:51:22,865::train::INFO] [train] Iter 599657 | loss 0.1506 | loss(rot) 0.0596 | loss(pos) 0.0171 | loss(seq) 0.0738 | grad 1.6863 | lr 0.0000 | time_forward 3.9510 | time_backward 5.4470 |
[2023-10-25 19:51:25,809::train::INFO] [train] Iter 599658 | loss 0.2002 | loss(rot) 0.1464 | loss(pos) 0.0250 | loss(seq) 0.0289 | grad 2.1332 | lr 0.0000 | time_forward 1.3510 | time_backward 1.5890 |
[2023-10-25 19:51:34,073::train::INFO] [train] Iter 599659 | loss 1.1948 | loss(rot) 0.7187 | loss(pos) 0.1307 | loss(seq) 0.3454 | grad 5.5752 | lr 0.0000 | time_forward 3.3890 | time_backward 4.8580 |
[2023-10-25 19:51:37,029::train::INFO] [train] Iter 599660 | loss 1.7648 | loss(rot) 1.7237 | loss(pos) 0.0411 | loss(seq) 0.0000 | grad 3.5777 | lr 0.0000 | time_forward 1.3480 | time_backward 1.6040 |
[2023-10-25 19:51:47,079::train::INFO] [train] Iter 599661 | loss 0.2132 | loss(rot) 0.0406 | loss(pos) 0.0896 | loss(seq) 0.0831 | grad 2.1171 | lr 0.0000 | time_forward 4.1720 | time_backward 5.8750 |
[2023-10-25 19:51:49,903::train::INFO] [train] Iter 599662 | loss 0.6641 | loss(rot) 0.4467 | loss(pos) 0.0124 | loss(seq) 0.2049 | grad 13.8596 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4950 |
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