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[2023-10-23 04:02:13,700::train::INFO] [train] Iter 565987 | loss 1.2601 | loss(rot) 0.9017 | loss(pos) 0.0463 | loss(seq) 0.3121 | grad 20.3771 | lr 0.0000 | time_forward 4.0550 | time_backward 4.8480 |
[2023-10-23 04:02:20,912::train::INFO] [train] Iter 565988 | loss 0.9322 | loss(rot) 0.2217 | loss(pos) 0.2230 | loss(seq) 0.4876 | grad 3.6262 | lr 0.0000 | time_forward 3.2010 | time_backward 4.0070 |
[2023-10-23 04:02:29,951::train::INFO] [train] Iter 565989 | loss 0.8816 | loss(rot) 0.7873 | loss(pos) 0.0383 | loss(seq) 0.0559 | grad 3.5250 | lr 0.0000 | time_forward 4.2030 | time_backward 4.8320 |
[2023-10-23 04:02:32,725::train::INFO] [train] Iter 565990 | loss 1.6576 | loss(rot) 1.5544 | loss(pos) 0.0627 | loss(seq) 0.0405 | grad 2.7772 | lr 0.0000 | time_forward 1.3020 | time_backward 1.4690 |
[2023-10-23 04:02:35,524::train::INFO] [train] Iter 565991 | loss 0.8036 | loss(rot) 0.0486 | loss(pos) 0.7291 | loss(seq) 0.0259 | grad 12.7061 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4680 |
[2023-10-23 04:02:38,314::train::INFO] [train] Iter 565992 | loss 1.6345 | loss(rot) 1.1680 | loss(pos) 0.1161 | loss(seq) 0.3504 | grad 4.6689 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4540 |
[2023-10-23 04:02:47,570::train::INFO] [train] Iter 565993 | loss 0.6421 | loss(rot) 0.1577 | loss(pos) 0.4685 | loss(seq) 0.0159 | grad 5.5064 | lr 0.0000 | time_forward 4.0920 | time_backward 5.1610 |
[2023-10-23 04:02:56,325::train::INFO] [train] Iter 565994 | loss 0.4448 | loss(rot) 0.1243 | loss(pos) 0.0346 | loss(seq) 0.2858 | grad 2.5622 | lr 0.0000 | time_forward 3.7230 | time_backward 5.0300 |
[2023-10-23 04:02:58,462::train::INFO] [train] Iter 565995 | loss 0.9937 | loss(rot) 0.2616 | loss(pos) 0.2849 | loss(seq) 0.4472 | grad 4.2645 | lr 0.0000 | time_forward 1.0170 | time_backward 1.1160 |
[2023-10-23 04:03:08,098::train::INFO] [train] Iter 565996 | loss 0.4338 | loss(rot) 0.1150 | loss(pos) 0.0422 | loss(seq) 0.2766 | grad 2.6396 | lr 0.0000 | time_forward 4.4720 | time_backward 5.1600 |
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