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[2023-10-25 12:45:37,599::train::INFO] [train] Iter 595957 | loss 1.7197 | loss(rot) 0.2943 | loss(pos) 1.4001 | loss(seq) 0.0253 | grad 10.7578 | lr 0.0000 | time_forward 1.3540 | time_backward 1.4830 |
[2023-10-25 12:45:40,404::train::INFO] [train] Iter 595958 | loss 0.1904 | loss(rot) 0.1036 | loss(pos) 0.0215 | loss(seq) 0.0653 | grad 1.5250 | lr 0.0000 | time_forward 1.3380 | time_backward 1.4570 |
[2023-10-25 12:45:49,028::train::INFO] [train] Iter 595959 | loss 0.2203 | loss(rot) 0.1063 | loss(pos) 0.0079 | loss(seq) 0.1061 | grad 2.1286 | lr 0.0000 | time_forward 3.5030 | time_backward 5.1190 |
[2023-10-25 12:45:57,528::train::INFO] [train] Iter 595960 | loss 0.3756 | loss(rot) 0.0643 | loss(pos) 0.3077 | loss(seq) 0.0036 | grad 7.3365 | lr 0.0000 | time_forward 3.6170 | time_backward 4.8790 |
[2023-10-25 12:46:07,630::train::INFO] [train] Iter 595961 | loss 0.4327 | loss(rot) 0.0685 | loss(pos) 0.2587 | loss(seq) 0.1056 | grad 5.4842 | lr 0.0000 | time_forward 4.0300 | time_backward 6.0690 |
[2023-10-25 12:46:10,521::train::INFO] [train] Iter 595962 | loss 0.2407 | loss(rot) 0.0629 | loss(pos) 0.0198 | loss(seq) 0.1579 | grad 1.8376 | lr 0.0000 | time_forward 1.3700 | time_backward 1.5180 |
[2023-10-25 12:46:13,333::train::INFO] [train] Iter 595963 | loss 0.9198 | loss(rot) 0.2379 | loss(pos) 0.4663 | loss(seq) 0.2155 | grad 4.5658 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4580 |
[2023-10-25 12:46:23,155::train::INFO] [train] Iter 595964 | loss 1.2726 | loss(rot) 0.6732 | loss(pos) 0.0754 | loss(seq) 0.5240 | grad 3.3801 | lr 0.0000 | time_forward 4.0030 | time_backward 5.8150 |
[2023-10-25 12:46:33,155::train::INFO] [train] Iter 595965 | loss 0.4544 | loss(rot) 0.1828 | loss(pos) 0.1253 | loss(seq) 0.1463 | grad 3.7687 | lr 0.0000 | time_forward 3.9830 | time_backward 6.0150 |
[2023-10-25 12:46:35,713::train::INFO] [train] Iter 595966 | loss 0.8735 | loss(rot) 0.6014 | loss(pos) 0.0233 | loss(seq) 0.2489 | grad 3.8226 | lr 0.0000 | time_forward 1.2300 | time_backward 1.3240 |
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