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[2023-10-23 09:01:40,921::train::INFO] [train] Iter 568984 | loss 0.4332 | loss(rot) 0.0719 | loss(pos) 0.0488 | loss(seq) 0.3124 | grad 2.6126 | lr 0.0000 | time_forward 1.2990 | time_backward 1.4210 |
[2023-10-23 09:01:46,992::train::INFO] [train] Iter 568985 | loss 0.5704 | loss(rot) 0.2846 | loss(pos) 0.1000 | loss(seq) 0.1858 | grad 4.7074 | lr 0.0000 | time_forward 2.6550 | time_backward 3.4140 |
[2023-10-23 09:01:54,493::train::INFO] [train] Iter 568986 | loss 0.3729 | loss(rot) 0.0886 | loss(pos) 0.1417 | loss(seq) 0.1425 | grad 6.1852 | lr 0.0000 | time_forward 3.2730 | time_backward 4.2250 |
[2023-10-23 09:02:02,634::train::INFO] [train] Iter 568987 | loss 0.3952 | loss(rot) 0.0924 | loss(pos) 0.0622 | loss(seq) 0.2405 | grad 3.0172 | lr 0.0000 | time_forward 3.3480 | time_backward 4.7890 |
[2023-10-23 09:02:05,427::train::INFO] [train] Iter 568988 | loss 1.8995 | loss(rot) 1.5881 | loss(pos) 0.0593 | loss(seq) 0.2521 | grad 16.5330 | lr 0.0000 | time_forward 1.2990 | time_backward 1.4910 |
[2023-10-23 09:02:13,590::train::INFO] [train] Iter 568989 | loss 0.2668 | loss(rot) 0.1108 | loss(pos) 0.1366 | loss(seq) 0.0195 | grad 2.9539 | lr 0.0000 | time_forward 3.3390 | time_backward 4.8210 |
[2023-10-23 09:02:21,231::train::INFO] [train] Iter 568990 | loss 1.2118 | loss(rot) 0.8811 | loss(pos) 0.0444 | loss(seq) 0.2863 | grad 3.2772 | lr 0.0000 | time_forward 3.3140 | time_backward 4.3230 |
[2023-10-23 09:02:29,507::train::INFO] [train] Iter 568991 | loss 0.6417 | loss(rot) 0.0144 | loss(pos) 0.6259 | loss(seq) 0.0014 | grad 6.3875 | lr 0.0000 | time_forward 3.3800 | time_backward 4.8930 |
[2023-10-23 09:02:36,787::train::INFO] [train] Iter 568992 | loss 0.4382 | loss(rot) 0.0724 | loss(pos) 0.0373 | loss(seq) 0.3285 | grad 2.9081 | lr 0.0000 | time_forward 3.1530 | time_backward 4.1240 |
[2023-10-23 09:02:39,220::train::INFO] [train] Iter 568993 | loss 0.4107 | loss(rot) 0.3369 | loss(pos) 0.0397 | loss(seq) 0.0341 | grad 3.3062 | lr 0.0000 | time_forward 1.1860 | time_backward 1.2440 |
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