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[2023-10-24 01:09:17,255::train::INFO] [train] Iter 577475 | loss 0.4878 | loss(rot) 0.1724 | loss(pos) 0.0605 | loss(seq) 0.2548 | grad 3.2543 | lr 0.0000 | time_forward 3.6400 | time_backward 4.9890 |
[2023-10-24 01:09:25,132::train::INFO] [train] Iter 577476 | loss 0.8923 | loss(rot) 0.1393 | loss(pos) 0.7507 | loss(seq) 0.0023 | grad 9.8861 | lr 0.0000 | time_forward 3.3380 | time_backward 4.5350 |
[2023-10-24 01:09:32,562::train::INFO] [train] Iter 577477 | loss 1.9217 | loss(rot) 1.7053 | loss(pos) 0.0417 | loss(seq) 0.1746 | grad 4.6268 | lr 0.0000 | time_forward 3.1640 | time_backward 4.2620 |
[2023-10-24 01:09:35,373::train::INFO] [train] Iter 577478 | loss 0.1552 | loss(rot) 0.1179 | loss(pos) 0.0120 | loss(seq) 0.0253 | grad 1.7541 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4730 |
[2023-10-24 01:09:45,218::train::INFO] [train] Iter 577479 | loss 0.5602 | loss(rot) 0.4532 | loss(pos) 0.0247 | loss(seq) 0.0823 | grad 3.1101 | lr 0.0000 | time_forward 4.0030 | time_backward 5.8400 |
[2023-10-24 01:09:52,714::train::INFO] [train] Iter 577480 | loss 0.2950 | loss(rot) 0.1520 | loss(pos) 0.0252 | loss(seq) 0.1178 | grad 2.7704 | lr 0.0000 | time_forward 3.2050 | time_backward 4.2870 |
[2023-10-24 01:10:01,669::train::INFO] [train] Iter 577481 | loss 0.3426 | loss(rot) 0.0951 | loss(pos) 0.0611 | loss(seq) 0.1864 | grad 3.5193 | lr 0.0000 | time_forward 3.8010 | time_backward 5.1510 |
[2023-10-24 01:10:10,777::train::INFO] [train] Iter 577482 | loss 0.2469 | loss(rot) 0.1831 | loss(pos) 0.0429 | loss(seq) 0.0209 | grad 2.4502 | lr 0.0000 | time_forward 3.8120 | time_backward 5.2920 |
[2023-10-24 01:10:19,448::train::INFO] [train] Iter 577483 | loss 1.2497 | loss(rot) 0.9427 | loss(pos) 0.0730 | loss(seq) 0.2341 | grad 6.5095 | lr 0.0000 | time_forward 3.6930 | time_backward 4.9750 |
[2023-10-24 01:10:22,386::train::INFO] [train] Iter 577484 | loss 0.4938 | loss(rot) 0.0304 | loss(pos) 0.4493 | loss(seq) 0.0141 | grad 7.3520 | lr 0.0000 | time_forward 1.3470 | time_backward 1.5890 |
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