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[2023-10-24 22:40:30,137::train::INFO] [train] Iter 588564 | loss 0.6079 | loss(rot) 0.1454 | loss(pos) 0.1594 | loss(seq) 0.3030 | grad 4.5170 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4700 |
[2023-10-24 22:40:32,676::train::INFO] [train] Iter 588565 | loss 0.4912 | loss(rot) 0.4408 | loss(pos) 0.0412 | loss(seq) 0.0092 | grad 6.3982 | lr 0.0000 | time_forward 1.2440 | time_backward 1.2910 |
[2023-10-24 22:40:42,054::train::INFO] [train] Iter 588566 | loss 0.4330 | loss(rot) 0.0574 | loss(pos) 0.1730 | loss(seq) 0.2026 | grad 3.5658 | lr 0.0000 | time_forward 3.9670 | time_backward 5.4080 |
[2023-10-24 22:40:44,345::train::INFO] [train] Iter 588567 | loss 0.5580 | loss(rot) 0.5003 | loss(pos) 0.0577 | loss(seq) 0.0001 | grad 6.9882 | lr 0.0000 | time_forward 1.0500 | time_backward 1.2370 |
[2023-10-24 22:40:47,153::train::INFO] [train] Iter 588568 | loss 0.5799 | loss(rot) 0.5156 | loss(pos) 0.0208 | loss(seq) 0.0434 | grad 2.6058 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4670 |
[2023-10-24 22:40:48,760::train::INFO] [train] Iter 588569 | loss 1.6242 | loss(rot) 0.1859 | loss(pos) 1.4230 | loss(seq) 0.0152 | grad 8.1236 | lr 0.0000 | time_forward 0.7610 | time_backward 0.8430 |
[2023-10-24 22:40:58,783::train::INFO] [train] Iter 588570 | loss 1.1964 | loss(rot) 0.5818 | loss(pos) 0.3289 | loss(seq) 0.2857 | grad 3.0290 | lr 0.0000 | time_forward 4.0440 | time_backward 5.9760 |
[2023-10-24 22:41:08,827::train::INFO] [train] Iter 588571 | loss 0.6648 | loss(rot) 0.4436 | loss(pos) 0.0270 | loss(seq) 0.1942 | grad 45.9195 | lr 0.0000 | time_forward 4.0520 | time_backward 5.9890 |
[2023-10-24 22:41:17,959::train::INFO] [train] Iter 588572 | loss 0.4863 | loss(rot) 0.0347 | loss(pos) 0.1560 | loss(seq) 0.2956 | grad 4.6476 | lr 0.0000 | time_forward 3.8560 | time_backward 5.2730 |
[2023-10-24 22:41:27,198::train::INFO] [train] Iter 588573 | loss 0.2135 | loss(rot) 0.1417 | loss(pos) 0.0285 | loss(seq) 0.0432 | grad 2.2738 | lr 0.0000 | time_forward 3.8950 | time_backward 5.3410 |
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