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[2023-10-23 16:48:11,485::train::INFO] [train] Iter 573179 | loss 0.5967 | loss(rot) 0.4995 | loss(pos) 0.0287 | loss(seq) 0.0685 | grad 3.3752 | lr 0.0000 | time_forward 4.0950 | time_backward 4.8520 |
[2023-10-23 16:48:14,275::train::INFO] [train] Iter 573180 | loss 0.8553 | loss(rot) 0.4404 | loss(pos) 0.0744 | loss(seq) 0.3405 | grad 3.1201 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4520 |
[2023-10-23 16:48:23,892::train::INFO] [train] Iter 573181 | loss 0.5079 | loss(rot) 0.1512 | loss(pos) 0.0435 | loss(seq) 0.3133 | grad 2.7512 | lr 0.0000 | time_forward 4.2360 | time_backward 5.3770 |
[2023-10-23 16:48:31,986::train::INFO] [train] Iter 573182 | loss 0.4676 | loss(rot) 0.2529 | loss(pos) 0.0361 | loss(seq) 0.1786 | grad 3.3003 | lr 0.0000 | time_forward 3.6970 | time_backward 4.3950 |
[2023-10-23 16:48:40,234::train::INFO] [train] Iter 573183 | loss 0.5133 | loss(rot) 0.0647 | loss(pos) 0.4231 | loss(seq) 0.0255 | grad 5.3052 | lr 0.0000 | time_forward 3.4610 | time_backward 4.7850 |
[2023-10-23 16:48:48,543::train::INFO] [train] Iter 573184 | loss 0.4057 | loss(rot) 0.3794 | loss(pos) 0.0261 | loss(seq) 0.0002 | grad 4.4198 | lr 0.0000 | time_forward 3.5430 | time_backward 4.7630 |
[2023-10-23 16:48:57,252::train::INFO] [train] Iter 573185 | loss 0.1155 | loss(rot) 0.1034 | loss(pos) 0.0120 | loss(seq) 0.0001 | grad 1.5338 | lr 0.0000 | time_forward 4.0200 | time_backward 4.6860 |
[2023-10-23 16:49:04,315::train::INFO] [train] Iter 573186 | loss 0.1458 | loss(rot) 0.0836 | loss(pos) 0.0169 | loss(seq) 0.0452 | grad 1.4158 | lr 0.0000 | time_forward 3.2380 | time_backward 3.8210 |
[2023-10-23 16:49:12,045::train::INFO] [train] Iter 573187 | loss 3.7486 | loss(rot) 0.0101 | loss(pos) 3.7385 | loss(seq) 0.0000 | grad 38.8933 | lr 0.0000 | time_forward 3.3090 | time_backward 4.4180 |
[2023-10-23 16:49:19,919::train::INFO] [train] Iter 573188 | loss 0.4403 | loss(rot) 0.1113 | loss(pos) 0.0552 | loss(seq) 0.2738 | grad 2.6347 | lr 0.0000 | time_forward 3.2990 | time_backward 4.5720 |
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