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[2023-10-25 04:58:59,764::train::INFO] [train] Iter 591861 | loss 1.1044 | loss(rot) 0.0051 | loss(pos) 1.0982 | loss(seq) 0.0010 | grad 10.0864 | lr 0.0000 | time_forward 1.2620 | time_backward 1.3880 |
[2023-10-25 04:59:07,543::train::INFO] [train] Iter 591862 | loss 0.5013 | loss(rot) 0.0450 | loss(pos) 0.1704 | loss(seq) 0.2859 | grad 5.8951 | lr 0.0000 | time_forward 3.3430 | time_backward 4.4320 |
[2023-10-25 04:59:16,320::train::INFO] [train] Iter 591863 | loss 0.5671 | loss(rot) 0.5153 | loss(pos) 0.0485 | loss(seq) 0.0034 | grad 4.2917 | lr 0.0000 | time_forward 3.5930 | time_backward 5.1810 |
[2023-10-25 04:59:19,523::train::INFO] [train] Iter 591864 | loss 0.9933 | loss(rot) 0.3603 | loss(pos) 0.1326 | loss(seq) 0.5004 | grad 3.0194 | lr 0.0000 | time_forward 1.4320 | time_backward 1.7680 |
[2023-10-25 04:59:28,762::train::INFO] [train] Iter 591865 | loss 0.8126 | loss(rot) 0.3338 | loss(pos) 0.1060 | loss(seq) 0.3728 | grad 3.7352 | lr 0.0000 | time_forward 3.7980 | time_backward 5.4260 |
[2023-10-25 04:59:36,698::train::INFO] [train] Iter 591866 | loss 1.9214 | loss(rot) 1.6972 | loss(pos) 0.0576 | loss(seq) 0.1666 | grad 5.3247 | lr 0.0000 | time_forward 3.4370 | time_backward 4.4960 |
[2023-10-25 04:59:39,404::train::INFO] [train] Iter 591867 | loss 0.9234 | loss(rot) 0.5554 | loss(pos) 0.0541 | loss(seq) 0.3138 | grad 2.8998 | lr 0.0000 | time_forward 1.3160 | time_backward 1.3870 |
[2023-10-25 04:59:42,189::train::INFO] [train] Iter 591868 | loss 2.2535 | loss(rot) 1.6834 | loss(pos) 0.2039 | loss(seq) 0.3662 | grad 10.0093 | lr 0.0000 | time_forward 1.2960 | time_backward 1.4680 |
[2023-10-25 04:59:50,147::train::INFO] [train] Iter 591869 | loss 0.6798 | loss(rot) 0.3998 | loss(pos) 0.0343 | loss(seq) 0.2456 | grad 3.5065 | lr 0.0000 | time_forward 3.4560 | time_backward 4.4790 |
[2023-10-25 04:59:52,884::train::INFO] [train] Iter 591870 | loss 0.4665 | loss(rot) 0.0172 | loss(pos) 0.4454 | loss(seq) 0.0039 | grad 6.7961 | lr 0.0000 | time_forward 1.3300 | time_backward 1.4040 |
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