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[2023-10-25 17:56:14,180::train::INFO] [train] Iter 598654 | loss 0.4630 | loss(rot) 0.1402 | loss(pos) 0.0193 | loss(seq) 0.3035 | grad 2.2407 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4460 |
[2023-10-25 17:56:16,981::train::INFO] [train] Iter 598655 | loss 0.8323 | loss(rot) 0.5468 | loss(pos) 0.2516 | loss(seq) 0.0339 | grad 5.2012 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4670 |
[2023-10-25 17:56:23,717::train::INFO] [train] Iter 598656 | loss 0.9909 | loss(rot) 0.7404 | loss(pos) 0.0772 | loss(seq) 0.1733 | grad 5.4432 | lr 0.0000 | time_forward 2.8690 | time_backward 3.8630 |
[2023-10-25 17:56:31,023::train::INFO] [train] Iter 598657 | loss 0.1248 | loss(rot) 0.0720 | loss(pos) 0.0071 | loss(seq) 0.0458 | grad 2.8361 | lr 0.0000 | time_forward 3.1030 | time_backward 4.2010 |
[2023-10-25 17:56:37,839::train::INFO] [train] Iter 598658 | loss 0.3861 | loss(rot) 0.0898 | loss(pos) 0.0248 | loss(seq) 0.2715 | grad 1.9624 | lr 0.0000 | time_forward 3.0440 | time_backward 3.7690 |
[2023-10-25 17:56:40,503::train::INFO] [train] Iter 598659 | loss 0.2404 | loss(rot) 0.1286 | loss(pos) 0.0316 | loss(seq) 0.0802 | grad 1.8139 | lr 0.0000 | time_forward 1.2410 | time_backward 1.4190 |
[2023-10-25 17:56:48,605::train::INFO] [train] Iter 598660 | loss 0.3656 | loss(rot) 0.3375 | loss(pos) 0.0264 | loss(seq) 0.0018 | grad 2.4497 | lr 0.0000 | time_forward 3.4840 | time_backward 4.6140 |
[2023-10-25 17:56:57,485::train::INFO] [train] Iter 598661 | loss 0.9741 | loss(rot) 0.4738 | loss(pos) 0.1087 | loss(seq) 0.3916 | grad 4.2267 | lr 0.0000 | time_forward 3.8130 | time_backward 5.0650 |
[2023-10-25 17:56:59,761::train::INFO] [train] Iter 598662 | loss 0.6046 | loss(rot) 0.5618 | loss(pos) 0.0418 | loss(seq) 0.0010 | grad 2.9303 | lr 0.0000 | time_forward 1.0350 | time_backward 1.2370 |
[2023-10-25 17:57:03,044::train::INFO] [train] Iter 598663 | loss 1.0984 | loss(rot) 0.6182 | loss(pos) 0.1018 | loss(seq) 0.3784 | grad 4.3144 | lr 0.0000 | time_forward 1.4680 | time_backward 1.8120 |
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