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[2023-10-24 21:16:00,411::train::INFO] [train] Iter 587865 | loss 0.8104 | loss(rot) 0.2784 | loss(pos) 0.0502 | loss(seq) 0.4818 | grad 3.3682 | lr 0.0000 | time_forward 3.9740 | time_backward 5.4610 |
[2023-10-24 21:16:07,803::train::INFO] [train] Iter 587866 | loss 0.4432 | loss(rot) 0.1482 | loss(pos) 0.0346 | loss(seq) 0.2604 | grad 2.6232 | lr 0.0000 | time_forward 3.0770 | time_backward 4.3130 |
[2023-10-24 21:16:16,282::train::INFO] [train] Iter 587867 | loss 1.2320 | loss(rot) 0.4778 | loss(pos) 0.2011 | loss(seq) 0.5531 | grad 4.6688 | lr 0.0000 | time_forward 3.8590 | time_backward 4.6160 |
[2023-10-24 21:16:22,483::train::INFO] [train] Iter 587868 | loss 1.4850 | loss(rot) 0.8723 | loss(pos) 0.1073 | loss(seq) 0.5054 | grad 3.5067 | lr 0.0000 | time_forward 2.6530 | time_backward 3.5460 |
[2023-10-24 21:16:30,813::train::INFO] [train] Iter 587869 | loss 0.9223 | loss(rot) 0.7275 | loss(pos) 0.0250 | loss(seq) 0.1698 | grad 3.9019 | lr 0.0000 | time_forward 3.9680 | time_backward 4.3590 |
[2023-10-24 21:16:33,534::train::INFO] [train] Iter 587870 | loss 0.6731 | loss(rot) 0.3341 | loss(pos) 0.0440 | loss(seq) 0.2950 | grad 3.9497 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4040 |
[2023-10-24 21:16:43,832::train::INFO] [train] Iter 587871 | loss 0.7969 | loss(rot) 0.5891 | loss(pos) 0.1990 | loss(seq) 0.0089 | grad 4.1090 | lr 0.0000 | time_forward 4.9620 | time_backward 5.3020 |
[2023-10-24 21:16:52,721::train::INFO] [train] Iter 587872 | loss 4.1355 | loss(rot) 0.0212 | loss(pos) 4.1143 | loss(seq) 0.0000 | grad 39.6377 | lr 0.0000 | time_forward 3.6300 | time_backward 5.2560 |
[2023-10-24 21:17:00,551::train::INFO] [train] Iter 587873 | loss 0.5765 | loss(rot) 0.1370 | loss(pos) 0.0175 | loss(seq) 0.4221 | grad 3.3225 | lr 0.0000 | time_forward 3.3210 | time_backward 4.5050 |
[2023-10-24 21:17:08,062::train::INFO] [train] Iter 587874 | loss 3.7086 | loss(rot) 0.0040 | loss(pos) 3.7046 | loss(seq) 0.0000 | grad 17.5881 | lr 0.0000 | time_forward 3.1860 | time_backward 4.3220 |
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