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[2023-09-01 22:13:41,065::train::INFO] [train] Iter 02883 | loss 2.5719 | loss(rot) 2.4379 | loss(pos) 0.1145 | loss(seq) 0.0195 | grad 4.5678 | lr 0.0010 | time_forward 3.8880 | time_backward 5.3470 |
[2023-09-01 22:13:48,541::train::INFO] [train] Iter 02884 | loss 2.2021 | loss(rot) 1.4803 | loss(pos) 0.2986 | loss(seq) 0.4232 | grad 4.6881 | lr 0.0010 | time_forward 3.1480 | time_backward 4.3220 |
[2023-09-01 22:13:57,241::train::INFO] [train] Iter 02885 | loss 1.7094 | loss(rot) 0.9712 | loss(pos) 0.3081 | loss(seq) 0.4300 | grad 4.4663 | lr 0.0010 | time_forward 3.7000 | time_backward 4.9970 |
[2023-09-01 22:13:59,884::train::INFO] [train] Iter 02886 | loss 1.7073 | loss(rot) 0.1741 | loss(pos) 1.2969 | loss(seq) 0.2364 | grad 5.8250 | lr 0.0010 | time_forward 1.2100 | time_backward 1.4290 |
[2023-09-01 22:14:08,425::train::INFO] [train] Iter 02887 | loss 2.6223 | loss(rot) 2.5070 | loss(pos) 0.1150 | loss(seq) 0.0003 | grad 5.0729 | lr 0.0010 | time_forward 3.4840 | time_backward 5.0280 |
[2023-09-01 22:14:18,364::train::INFO] [train] Iter 02888 | loss 3.0155 | loss(rot) 2.6174 | loss(pos) 0.2985 | loss(seq) 0.0996 | grad 4.2739 | lr 0.0010 | time_forward 4.1340 | time_backward 5.8020 |
[2023-09-01 22:14:21,004::train::INFO] [train] Iter 02889 | loss 2.1473 | loss(rot) 1.7833 | loss(pos) 0.2758 | loss(seq) 0.0882 | grad 6.9510 | lr 0.0010 | time_forward 1.2180 | time_backward 1.4180 |
[2023-09-01 22:14:30,802::train::INFO] [train] Iter 02890 | loss 4.6118 | loss(rot) 0.0146 | loss(pos) 4.5972 | loss(seq) 0.0000 | grad 6.7483 | lr 0.0010 | time_forward 4.0020 | time_backward 5.7930 |
[2023-09-01 22:14:38,739::train::INFO] [train] Iter 02891 | loss 3.3361 | loss(rot) 3.2275 | loss(pos) 0.0973 | loss(seq) 0.0113 | grad 3.8570 | lr 0.0010 | time_forward 3.3570 | time_backward 4.5540 |
[2023-09-01 22:14:47,767::train::INFO] [train] Iter 02892 | loss 2.9853 | loss(rot) 2.2925 | loss(pos) 0.3177 | loss(seq) 0.3751 | grad 4.8860 | lr 0.0010 | time_forward 3.8230 | time_backward 5.2030 |
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