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[2023-10-24 20:53:53,144::train::INFO] [train] Iter 587665 | loss 0.4499 | loss(rot) 0.3555 | loss(pos) 0.0197 | loss(seq) 0.0746 | grad 2.8393 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4680 |
[2023-10-24 20:54:00,701::train::INFO] [train] Iter 587666 | loss 0.3985 | loss(rot) 0.1116 | loss(pos) 0.0514 | loss(seq) 0.2355 | grad 2.6981 | lr 0.0000 | time_forward 3.1620 | time_backward 4.3920 |
[2023-10-24 20:54:03,518::train::INFO] [train] Iter 587667 | loss 0.6017 | loss(rot) 0.2744 | loss(pos) 0.0181 | loss(seq) 0.3093 | grad 7.2667 | lr 0.0000 | time_forward 1.3480 | time_backward 1.4670 |
[2023-10-24 20:54:12,697::train::INFO] [train] Iter 587668 | loss 0.9861 | loss(rot) 0.5653 | loss(pos) 0.2110 | loss(seq) 0.2099 | grad 3.8296 | lr 0.0000 | time_forward 3.8810 | time_backward 5.2940 |
[2023-10-24 20:54:17,281::train::INFO] [train] Iter 587669 | loss 1.0168 | loss(rot) 0.9922 | loss(pos) 0.0247 | loss(seq) 0.0000 | grad 7.2559 | lr 0.0000 | time_forward 2.0290 | time_backward 2.5530 |
[2023-10-24 20:54:25,531::train::INFO] [train] Iter 587670 | loss 0.5819 | loss(rot) 0.0589 | loss(pos) 0.5205 | loss(seq) 0.0025 | grad 7.6420 | lr 0.0000 | time_forward 3.5180 | time_backward 4.7280 |
[2023-10-24 20:54:35,631::train::INFO] [train] Iter 587671 | loss 0.3975 | loss(rot) 0.1079 | loss(pos) 0.0428 | loss(seq) 0.2468 | grad 2.2918 | lr 0.0000 | time_forward 4.0250 | time_backward 6.0120 |
[2023-10-24 20:54:44,383::train::INFO] [train] Iter 587672 | loss 1.5513 | loss(rot) 1.5019 | loss(pos) 0.0282 | loss(seq) 0.0212 | grad 3.2429 | lr 0.0000 | time_forward 3.6440 | time_backward 5.1050 |
[2023-10-24 20:54:53,222::train::INFO] [train] Iter 587673 | loss 0.4888 | loss(rot) 0.1925 | loss(pos) 0.0440 | loss(seq) 0.2524 | grad 3.5948 | lr 0.0000 | time_forward 3.7600 | time_backward 5.0760 |
[2023-10-24 20:55:02,419::train::INFO] [train] Iter 587674 | loss 1.7006 | loss(rot) 0.0902 | loss(pos) 1.6072 | loss(seq) 0.0032 | grad 11.0553 | lr 0.0000 | time_forward 3.8420 | time_backward 5.2750 |
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