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[2023-10-24 06:51:34,970::train::INFO] [train] Iter 580373 | loss 0.3899 | loss(rot) 0.0416 | loss(pos) 0.3420 | loss(seq) 0.0063 | grad 5.2709 | lr 0.0000 | time_forward 3.9290 | time_backward 5.5060 |
[2023-10-24 06:51:44,055::train::INFO] [train] Iter 580374 | loss 1.3772 | loss(rot) 0.7986 | loss(pos) 0.0903 | loss(seq) 0.4882 | grad 2.7543 | lr 0.0000 | time_forward 3.8870 | time_backward 5.1940 |
[2023-10-24 06:51:46,817::train::INFO] [train] Iter 580375 | loss 0.6852 | loss(rot) 0.0373 | loss(pos) 0.3140 | loss(seq) 0.3339 | grad 5.6979 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4640 |
[2023-10-24 06:51:49,682::train::INFO] [train] Iter 580376 | loss 1.1478 | loss(rot) 0.8506 | loss(pos) 0.0215 | loss(seq) 0.2757 | grad 8.0637 | lr 0.0000 | time_forward 1.3610 | time_backward 1.5000 |
[2023-10-24 06:51:53,367::train::INFO] [train] Iter 580377 | loss 2.2204 | loss(rot) 1.5950 | loss(pos) 0.2051 | loss(seq) 0.4203 | grad 10.6034 | lr 0.0000 | time_forward 1.7130 | time_backward 1.9680 |
[2023-10-24 06:52:01,670::train::INFO] [train] Iter 580378 | loss 0.5154 | loss(rot) 0.1934 | loss(pos) 0.0094 | loss(seq) 0.3126 | grad 3.2561 | lr 0.0000 | time_forward 3.4800 | time_backward 4.8010 |
[2023-10-24 06:52:05,045::train::INFO] [train] Iter 580379 | loss 0.7617 | loss(rot) 0.7223 | loss(pos) 0.0172 | loss(seq) 0.0221 | grad 5.0668 | lr 0.0000 | time_forward 1.5040 | time_backward 1.8470 |
[2023-10-24 06:52:13,181::train::INFO] [train] Iter 580380 | loss 0.4996 | loss(rot) 0.2925 | loss(pos) 0.0364 | loss(seq) 0.1706 | grad 3.1770 | lr 0.0000 | time_forward 3.4320 | time_backward 4.6890 |
[2023-10-24 06:52:21,263::train::INFO] [train] Iter 580381 | loss 0.2689 | loss(rot) 0.2381 | loss(pos) 0.0306 | loss(seq) 0.0001 | grad 4.9520 | lr 0.0000 | time_forward 3.4060 | time_backward 4.6730 |
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