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[2023-10-24 17:29:46,466::train::INFO] [train] Iter 585867 | loss 0.4143 | loss(rot) 0.2670 | loss(pos) 0.0319 | loss(seq) 0.1153 | grad 3.4706 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4240 |
[2023-10-24 17:29:54,663::train::INFO] [train] Iter 585868 | loss 0.5730 | loss(rot) 0.0601 | loss(pos) 0.5077 | loss(seq) 0.0052 | grad 12.4848 | lr 0.0000 | time_forward 3.5010 | time_backward 4.6600 |
[2023-10-24 17:29:57,433::train::INFO] [train] Iter 585869 | loss 0.6071 | loss(rot) 0.5881 | loss(pos) 0.0186 | loss(seq) 0.0005 | grad 44.6470 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4520 |
[2023-10-24 17:30:06,076::train::INFO] [train] Iter 585870 | loss 0.5272 | loss(rot) 0.1238 | loss(pos) 0.2396 | loss(seq) 0.1638 | grad 3.6971 | lr 0.0000 | time_forward 3.6000 | time_backward 5.0400 |
[2023-10-24 17:30:12,686::train::INFO] [train] Iter 585871 | loss 0.6325 | loss(rot) 0.3377 | loss(pos) 0.1552 | loss(seq) 0.1396 | grad 3.4293 | lr 0.0000 | time_forward 2.8180 | time_backward 3.7900 |
[2023-10-24 17:30:20,452::train::INFO] [train] Iter 585872 | loss 1.1516 | loss(rot) 0.6412 | loss(pos) 0.2889 | loss(seq) 0.2215 | grad 6.0012 | lr 0.0000 | time_forward 3.3220 | time_backward 4.4410 |
[2023-10-24 17:30:27,933::train::INFO] [train] Iter 585873 | loss 1.7387 | loss(rot) 1.1987 | loss(pos) 0.1197 | loss(seq) 0.4203 | grad 20.6227 | lr 0.0000 | time_forward 3.2110 | time_backward 4.2650 |
[2023-10-24 17:30:36,665::train::INFO] [train] Iter 585874 | loss 0.9479 | loss(rot) 0.2008 | loss(pos) 0.6872 | loss(seq) 0.0599 | grad 8.5591 | lr 0.0000 | time_forward 3.5840 | time_backward 5.1450 |
[2023-10-24 17:30:44,220::train::INFO] [train] Iter 585875 | loss 1.4006 | loss(rot) 1.0548 | loss(pos) 0.0624 | loss(seq) 0.2835 | grad 7.1899 | lr 0.0000 | time_forward 3.2440 | time_backward 4.3070 |
[2023-10-24 17:30:52,284::train::INFO] [train] Iter 585876 | loss 0.9366 | loss(rot) 0.0257 | loss(pos) 0.9103 | loss(seq) 0.0006 | grad 12.6651 | lr 0.0000 | time_forward 3.4690 | time_backward 4.5920 |
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