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[2023-10-25 03:53:01,923::train::INFO] [train] Iter 591261 | loss 1.7026 | loss(rot) 1.2796 | loss(pos) 0.0786 | loss(seq) 0.3445 | grad 3.4672 | lr 0.0000 | time_forward 3.7350 | time_backward 5.0790 |
[2023-10-25 03:53:04,640::train::INFO] [train] Iter 591262 | loss 0.3284 | loss(rot) 0.1737 | loss(pos) 0.0281 | loss(seq) 0.1266 | grad 3.0600 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4200 |
[2023-10-25 03:53:11,852::train::INFO] [train] Iter 591263 | loss 1.1798 | loss(rot) 0.0651 | loss(pos) 1.1057 | loss(seq) 0.0091 | grad 14.1087 | lr 0.0000 | time_forward 3.0590 | time_backward 4.1500 |
[2023-10-25 03:53:14,509::train::INFO] [train] Iter 591264 | loss 0.3752 | loss(rot) 0.2636 | loss(pos) 0.0243 | loss(seq) 0.0873 | grad 2.3470 | lr 0.0000 | time_forward 1.2770 | time_backward 1.3770 |
[2023-10-25 03:53:17,263::train::INFO] [train] Iter 591265 | loss 0.6542 | loss(rot) 0.6309 | loss(pos) 0.0155 | loss(seq) 0.0078 | grad 3.9782 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4130 |
[2023-10-25 03:53:26,091::train::INFO] [train] Iter 591266 | loss 1.3964 | loss(rot) 0.0553 | loss(pos) 1.3404 | loss(seq) 0.0007 | grad 14.2199 | lr 0.0000 | time_forward 3.6240 | time_backward 5.1960 |
[2023-10-25 03:53:31,319::train::INFO] [train] Iter 591267 | loss 0.2272 | loss(rot) 0.1970 | loss(pos) 0.0134 | loss(seq) 0.0168 | grad 3.1580 | lr 0.0000 | time_forward 2.2260 | time_backward 2.9800 |
[2023-10-25 03:53:40,641::train::INFO] [train] Iter 591268 | loss 0.5170 | loss(rot) 0.0290 | loss(pos) 0.4873 | loss(seq) 0.0007 | grad 7.4868 | lr 0.0000 | time_forward 3.6700 | time_backward 5.6480 |
[2023-10-25 03:53:48,872::train::INFO] [train] Iter 591269 | loss 0.4772 | loss(rot) 0.0132 | loss(pos) 0.4606 | loss(seq) 0.0034 | grad 8.1066 | lr 0.0000 | time_forward 3.5200 | time_backward 4.7070 |
[2023-10-25 03:53:57,759::train::INFO] [train] Iter 591270 | loss 0.4228 | loss(rot) 0.0708 | loss(pos) 0.0823 | loss(seq) 0.2697 | grad 3.5109 | lr 0.0000 | time_forward 3.6730 | time_backward 5.2120 |
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