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[2023-10-23 15:20:50,948::train::INFO] [train] Iter 572380 | loss 0.9335 | loss(rot) 0.5266 | loss(pos) 0.0784 | loss(seq) 0.3285 | grad 15.0249 | lr 0.0000 | time_forward 3.3610 | time_backward 5.5410 |
[2023-10-23 15:20:54,360::train::INFO] [train] Iter 572381 | loss 0.9545 | loss(rot) 0.3046 | loss(pos) 0.0868 | loss(seq) 0.5630 | grad 4.1950 | lr 0.0000 | time_forward 1.7030 | time_backward 1.7050 |
[2023-10-23 15:20:57,147::train::INFO] [train] Iter 572382 | loss 0.3494 | loss(rot) 0.1606 | loss(pos) 0.0278 | loss(seq) 0.1611 | grad 2.0950 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4460 |
[2023-10-23 15:21:05,038::train::INFO] [train] Iter 572383 | loss 0.5865 | loss(rot) 0.3429 | loss(pos) 0.0304 | loss(seq) 0.2132 | grad 39.5810 | lr 0.0000 | time_forward 3.2270 | time_backward 4.6610 |
[2023-10-23 15:21:12,817::train::INFO] [train] Iter 572384 | loss 2.3596 | loss(rot) 1.5487 | loss(pos) 0.3691 | loss(seq) 0.4419 | grad 5.3902 | lr 0.0000 | time_forward 3.2110 | time_backward 4.5550 |
[2023-10-23 15:21:19,350::train::INFO] [train] Iter 572385 | loss 0.7397 | loss(rot) 0.0429 | loss(pos) 0.6876 | loss(seq) 0.0092 | grad 6.1996 | lr 0.0000 | time_forward 2.9230 | time_backward 3.6060 |
[2023-10-23 15:21:33,669::train::INFO] [train] Iter 572386 | loss 0.2099 | loss(rot) 0.1208 | loss(pos) 0.0412 | loss(seq) 0.0478 | grad 3.0194 | lr 0.0000 | time_forward 2.7110 | time_backward 11.6050 |
[2023-10-23 15:21:44,639::train::INFO] [train] Iter 572387 | loss 0.3544 | loss(rot) 0.1139 | loss(pos) 0.0549 | loss(seq) 0.1856 | grad 2.6762 | lr 0.0000 | time_forward 6.3960 | time_backward 4.5710 |
[2023-10-23 15:21:51,423::train::INFO] [train] Iter 572388 | loss 0.3487 | loss(rot) 0.3061 | loss(pos) 0.0195 | loss(seq) 0.0231 | grad 3.8483 | lr 0.0000 | time_forward 3.0090 | time_backward 3.7720 |
[2023-10-23 15:22:00,840::train::INFO] [train] Iter 572389 | loss 0.3685 | loss(rot) 0.1530 | loss(pos) 0.0218 | loss(seq) 0.1937 | grad 2.6766 | lr 0.0000 | time_forward 4.8720 | time_backward 4.5410 |
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