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[2023-10-23 08:32:21,826::train::INFO] [train] Iter 568684 | loss 0.2820 | loss(rot) 0.1251 | loss(pos) 0.1288 | loss(seq) 0.0282 | grad 3.6948 | lr 0.0000 | time_forward 3.0260 | time_backward 3.9730 |
[2023-10-23 08:32:29,414::train::INFO] [train] Iter 568685 | loss 0.7420 | loss(rot) 0.1200 | loss(pos) 0.4234 | loss(seq) 0.1985 | grad 5.0780 | lr 0.0000 | time_forward 3.3010 | time_backward 4.2850 |
[2023-10-23 08:32:37,640::train::INFO] [train] Iter 568686 | loss 0.7366 | loss(rot) 0.0350 | loss(pos) 0.6982 | loss(seq) 0.0033 | grad 8.7953 | lr 0.0000 | time_forward 3.4060 | time_backward 4.8160 |
[2023-10-23 08:32:40,345::train::INFO] [train] Iter 568687 | loss 0.8610 | loss(rot) 0.7905 | loss(pos) 0.0184 | loss(seq) 0.0521 | grad 8.7828 | lr 0.0000 | time_forward 1.2510 | time_backward 1.4520 |
[2023-10-23 08:32:46,851::train::INFO] [train] Iter 568688 | loss 0.3634 | loss(rot) 0.1469 | loss(pos) 0.0811 | loss(seq) 0.1355 | grad 3.8333 | lr 0.0000 | time_forward 2.7990 | time_backward 3.7030 |
[2023-10-23 08:32:54,483::train::INFO] [train] Iter 568689 | loss 0.2188 | loss(rot) 0.0994 | loss(pos) 0.0867 | loss(seq) 0.0328 | grad 2.1741 | lr 0.0000 | time_forward 3.2800 | time_backward 4.3490 |
[2023-10-23 08:33:02,214::train::INFO] [train] Iter 568690 | loss 1.7117 | loss(rot) 1.0819 | loss(pos) 0.3012 | loss(seq) 0.3287 | grad 8.6280 | lr 0.0000 | time_forward 3.4140 | time_backward 4.3130 |
[2023-10-23 08:33:04,890::train::INFO] [train] Iter 568691 | loss 0.2666 | loss(rot) 0.1078 | loss(pos) 0.0271 | loss(seq) 0.1318 | grad 2.1081 | lr 0.0000 | time_forward 1.2880 | time_backward 1.3850 |
[2023-10-23 08:33:11,950::train::INFO] [train] Iter 568692 | loss 0.5802 | loss(rot) 0.0496 | loss(pos) 0.2379 | loss(seq) 0.2928 | grad 5.5502 | lr 0.0000 | time_forward 3.0560 | time_backward 3.9850 |
[2023-10-23 08:33:20,156::train::INFO] [train] Iter 568693 | loss 0.3889 | loss(rot) 0.3067 | loss(pos) 0.0753 | loss(seq) 0.0069 | grad 2.8929 | lr 0.0000 | time_forward 3.5450 | time_backward 4.6570 |
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