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[2023-10-24 18:02:27,735::train::INFO] [train] Iter 586166 | loss 0.4506 | loss(rot) 0.1656 | loss(pos) 0.2751 | loss(seq) 0.0098 | grad 3.9083 | lr 0.0000 | time_forward 3.2880 | time_backward 4.1350 |
[2023-10-24 18:02:36,366::train::INFO] [train] Iter 586167 | loss 1.2774 | loss(rot) 1.2495 | loss(pos) 0.0279 | loss(seq) 0.0000 | grad 4.5340 | lr 0.0000 | time_forward 3.6390 | time_backward 4.9890 |
[2023-10-24 18:02:42,587::train::INFO] [train] Iter 586168 | loss 0.5398 | loss(rot) 0.5099 | loss(pos) 0.0291 | loss(seq) 0.0008 | grad 4.2924 | lr 0.0000 | time_forward 2.7090 | time_backward 3.5090 |
[2023-10-24 18:02:49,448::train::INFO] [train] Iter 586169 | loss 1.3283 | loss(rot) 0.0061 | loss(pos) 1.3219 | loss(seq) 0.0003 | grad 16.0586 | lr 0.0000 | time_forward 2.8210 | time_backward 4.0370 |
[2023-10-24 18:02:56,684::train::INFO] [train] Iter 586170 | loss 1.4136 | loss(rot) 1.3661 | loss(pos) 0.0475 | loss(seq) 0.0000 | grad 2.7586 | lr 0.0000 | time_forward 3.1730 | time_backward 4.0600 |
[2023-10-24 18:03:03,591::train::INFO] [train] Iter 586171 | loss 0.3816 | loss(rot) 0.2924 | loss(pos) 0.0235 | loss(seq) 0.0658 | grad 3.3881 | lr 0.0000 | time_forward 3.0090 | time_backward 3.8960 |
[2023-10-24 18:03:06,371::train::INFO] [train] Iter 586172 | loss 0.7226 | loss(rot) 0.1370 | loss(pos) 0.5769 | loss(seq) 0.0087 | grad 5.2547 | lr 0.0000 | time_forward 1.3400 | time_backward 1.4360 |
[2023-10-24 18:03:12,689::train::INFO] [train] Iter 586173 | loss 1.2516 | loss(rot) 0.6082 | loss(pos) 0.1837 | loss(seq) 0.4597 | grad 3.6242 | lr 0.0000 | time_forward 2.7240 | time_backward 3.5780 |
[2023-10-24 18:03:20,966::train::INFO] [train] Iter 586174 | loss 0.1876 | loss(rot) 0.0718 | loss(pos) 0.0225 | loss(seq) 0.0933 | grad 1.6200 | lr 0.0000 | time_forward 3.4000 | time_backward 4.8740 |
[2023-10-24 18:03:29,207::train::INFO] [train] Iter 586175 | loss 0.4702 | loss(rot) 0.2734 | loss(pos) 0.0210 | loss(seq) 0.1758 | grad 2.5463 | lr 0.0000 | time_forward 3.3210 | time_backward 4.9170 |
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