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[2023-10-25 05:20:29,905::train::INFO] [train] Iter 592060 | loss 0.7484 | loss(rot) 0.5460 | loss(pos) 0.0272 | loss(seq) 0.1751 | grad 3.4406 | lr 0.0000 | time_forward 3.7600 | time_backward 5.3380 |
[2023-10-25 05:20:32,635::train::INFO] [train] Iter 592061 | loss 0.3881 | loss(rot) 0.3662 | loss(pos) 0.0217 | loss(seq) 0.0001 | grad 2.3851 | lr 0.0000 | time_forward 1.2710 | time_backward 1.4560 |
[2023-10-25 05:20:39,049::train::INFO] [train] Iter 592062 | loss 0.4944 | loss(rot) 0.1733 | loss(pos) 0.2428 | loss(seq) 0.0784 | grad 3.8949 | lr 0.0000 | time_forward 2.7690 | time_backward 3.6370 |
[2023-10-25 05:20:41,769::train::INFO] [train] Iter 592063 | loss 0.1340 | loss(rot) 0.1108 | loss(pos) 0.0201 | loss(seq) 0.0031 | grad 1.6061 | lr 0.0000 | time_forward 1.2810 | time_backward 1.4350 |
[2023-10-25 05:20:44,470::train::INFO] [train] Iter 592064 | loss 0.1542 | loss(rot) 0.1077 | loss(pos) 0.0198 | loss(seq) 0.0267 | grad 1.8465 | lr 0.0000 | time_forward 1.3040 | time_backward 1.3950 |
[2023-10-25 05:20:51,712::train::INFO] [train] Iter 592065 | loss 0.5627 | loss(rot) 0.4246 | loss(pos) 0.0523 | loss(seq) 0.0859 | grad 2.4077 | lr 0.0000 | time_forward 3.0990 | time_backward 4.1380 |
[2023-10-25 05:20:59,521::train::INFO] [train] Iter 592066 | loss 0.3304 | loss(rot) 0.0759 | loss(pos) 0.0279 | loss(seq) 0.2265 | grad 2.2642 | lr 0.0000 | time_forward 3.3490 | time_backward 4.4560 |
[2023-10-25 05:21:08,479::train::INFO] [train] Iter 592067 | loss 1.0014 | loss(rot) 0.8838 | loss(pos) 0.0328 | loss(seq) 0.0848 | grad 2.9078 | lr 0.0000 | time_forward 3.6940 | time_backward 5.2620 |
[2023-10-25 05:21:11,254::train::INFO] [train] Iter 592068 | loss 0.7752 | loss(rot) 0.0192 | loss(pos) 0.7545 | loss(seq) 0.0015 | grad 11.6870 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4610 |
[2023-10-25 05:21:19,788::train::INFO] [train] Iter 592069 | loss 0.1383 | loss(rot) 0.0930 | loss(pos) 0.0450 | loss(seq) 0.0003 | grad 1.8766 | lr 0.0000 | time_forward 3.7030 | time_backward 4.8290 |
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