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[2023-10-23 21:10:47,193::train::INFO] [train] Iter 575477 | loss 1.5026 | loss(rot) 1.1843 | loss(pos) 0.0702 | loss(seq) 0.2481 | grad 3.4776 | lr 0.0000 | time_forward 2.7260 | time_backward 3.6060 |
[2023-10-23 21:10:53,409::train::INFO] [train] Iter 575478 | loss 0.5309 | loss(rot) 0.3515 | loss(pos) 0.0227 | loss(seq) 0.1566 | grad 2.4143 | lr 0.0000 | time_forward 2.7240 | time_backward 3.4880 |
[2023-10-23 21:11:01,026::train::INFO] [train] Iter 575479 | loss 1.0397 | loss(rot) 0.1401 | loss(pos) 0.8955 | loss(seq) 0.0040 | grad 5.0682 | lr 0.0000 | time_forward 3.3670 | time_backward 4.2480 |
[2023-10-23 21:11:09,060::train::INFO] [train] Iter 575480 | loss 0.5272 | loss(rot) 0.4483 | loss(pos) 0.0196 | loss(seq) 0.0592 | grad 2.8401 | lr 0.0000 | time_forward 3.2730 | time_backward 4.7570 |
[2023-10-23 21:11:17,115::train::INFO] [train] Iter 575481 | loss 1.1127 | loss(rot) 0.8494 | loss(pos) 0.0494 | loss(seq) 0.2139 | grad 4.4535 | lr 0.0000 | time_forward 3.3560 | time_backward 4.6960 |
[2023-10-23 21:11:24,694::train::INFO] [train] Iter 575482 | loss 0.5076 | loss(rot) 0.0560 | loss(pos) 0.0411 | loss(seq) 0.4105 | grad 2.6704 | lr 0.0000 | time_forward 3.2850 | time_backward 4.2900 |
[2023-10-23 21:11:27,437::train::INFO] [train] Iter 575483 | loss 1.3480 | loss(rot) 1.0814 | loss(pos) 0.0408 | loss(seq) 0.2258 | grad 18.1140 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4350 |
[2023-10-23 21:11:30,089::train::INFO] [train] Iter 575484 | loss 0.4179 | loss(rot) 0.0259 | loss(pos) 0.3898 | loss(seq) 0.0022 | grad 7.0310 | lr 0.0000 | time_forward 1.2540 | time_backward 1.3960 |
[2023-10-23 21:11:37,618::train::INFO] [train] Iter 575485 | loss 0.2328 | loss(rot) 0.0273 | loss(pos) 0.1998 | loss(seq) 0.0057 | grad 5.0922 | lr 0.0000 | time_forward 3.3750 | time_backward 4.1500 |
[2023-10-23 21:11:40,306::train::INFO] [train] Iter 575486 | loss 0.4946 | loss(rot) 0.1421 | loss(pos) 0.0570 | loss(seq) 0.2956 | grad 2.9474 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4070 |
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