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[2023-10-23 19:28:30,437::train::INFO] [train] Iter 574578 | loss 0.6759 | loss(rot) 0.1521 | loss(pos) 0.1236 | loss(seq) 0.4002 | grad 4.3602 | lr 0.0000 | time_forward 3.2470 | time_backward 4.4350 |
[2023-10-23 19:28:37,664::train::INFO] [train] Iter 574579 | loss 2.1662 | loss(rot) 1.8517 | loss(pos) 0.0893 | loss(seq) 0.2252 | grad 5.3529 | lr 0.0000 | time_forward 3.0780 | time_backward 4.1450 |
[2023-10-23 19:28:47,024::train::INFO] [train] Iter 574580 | loss 0.6439 | loss(rot) 0.0692 | loss(pos) 0.5662 | loss(seq) 0.0086 | grad 6.0585 | lr 0.0000 | time_forward 3.8710 | time_backward 5.4860 |
[2023-10-23 19:28:49,813::train::INFO] [train] Iter 574581 | loss 0.5515 | loss(rot) 0.5376 | loss(pos) 0.0135 | loss(seq) 0.0003 | grad 3.8989 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4520 |
[2023-10-23 19:28:58,562::train::INFO] [train] Iter 574582 | loss 0.6599 | loss(rot) 0.1387 | loss(pos) 0.3880 | loss(seq) 0.1333 | grad 4.8147 | lr 0.0000 | time_forward 3.7150 | time_backward 5.0000 |
[2023-10-23 19:29:07,869::train::INFO] [train] Iter 574583 | loss 0.4712 | loss(rot) 0.1123 | loss(pos) 0.1950 | loss(seq) 0.1638 | grad 3.9552 | lr 0.0000 | time_forward 3.8390 | time_backward 5.4650 |
[2023-10-23 19:29:15,914::train::INFO] [train] Iter 574584 | loss 1.3806 | loss(rot) 1.3073 | loss(pos) 0.0314 | loss(seq) 0.0419 | grad 3.8661 | lr 0.0000 | time_forward 3.4370 | time_backward 4.6050 |
[2023-10-23 19:29:24,086::train::INFO] [train] Iter 574585 | loss 1.6057 | loss(rot) 1.2353 | loss(pos) 0.0656 | loss(seq) 0.3048 | grad 3.5508 | lr 0.0000 | time_forward 3.4380 | time_backward 4.7310 |
[2023-10-23 19:29:29,718::train::INFO] [train] Iter 574586 | loss 0.8884 | loss(rot) 0.8627 | loss(pos) 0.0234 | loss(seq) 0.0023 | grad 3.1760 | lr 0.0000 | time_forward 2.4250 | time_backward 3.2040 |
[2023-10-23 19:29:37,912::train::INFO] [train] Iter 574587 | loss 0.3371 | loss(rot) 0.1293 | loss(pos) 0.1739 | loss(seq) 0.0339 | grad 2.4430 | lr 0.0000 | time_forward 3.4860 | time_backward 4.7040 |
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