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[2023-10-25 02:12:47,665::train::INFO] [train] Iter 590363 | loss 0.3302 | loss(rot) 0.0810 | loss(pos) 0.0397 | loss(seq) 0.2095 | grad 2.5345 | lr 0.0000 | time_forward 3.4840 | time_backward 4.6350 |
[2023-10-25 02:12:56,584::train::INFO] [train] Iter 590364 | loss 0.1386 | loss(rot) 0.0751 | loss(pos) 0.0286 | loss(seq) 0.0348 | grad 2.0965 | lr 0.0000 | time_forward 3.6620 | time_backward 5.2540 |
[2023-10-25 02:13:04,095::train::INFO] [train] Iter 590365 | loss 0.4479 | loss(rot) 0.0844 | loss(pos) 0.3571 | loss(seq) 0.0064 | grad 9.8059 | lr 0.0000 | time_forward 3.1860 | time_backward 4.3220 |
[2023-10-25 02:13:06,848::train::INFO] [train] Iter 590366 | loss 0.2986 | loss(rot) 0.0667 | loss(pos) 0.0140 | loss(seq) 0.2179 | grad 2.2451 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4390 |
[2023-10-25 02:13:15,825::train::INFO] [train] Iter 590367 | loss 1.5746 | loss(rot) 0.8059 | loss(pos) 0.1959 | loss(seq) 0.5728 | grad 3.4938 | lr 0.0000 | time_forward 3.7760 | time_backward 5.1650 |
[2023-10-25 02:13:19,069::train::INFO] [train] Iter 590368 | loss 0.4074 | loss(rot) 0.0841 | loss(pos) 0.3157 | loss(seq) 0.0076 | grad 4.5525 | lr 0.0000 | time_forward 1.4610 | time_backward 1.7780 |
[2023-10-25 02:13:23,380::train::INFO] [train] Iter 590369 | loss 1.5497 | loss(rot) 1.5008 | loss(pos) 0.0290 | loss(seq) 0.0198 | grad 8.6219 | lr 0.0000 | time_forward 1.9730 | time_backward 2.3220 |
[2023-10-25 02:13:29,962::train::INFO] [train] Iter 590370 | loss 1.1688 | loss(rot) 0.7709 | loss(pos) 0.0331 | loss(seq) 0.3648 | grad 3.9013 | lr 0.0000 | time_forward 2.8090 | time_backward 3.7690 |
[2023-10-25 02:13:39,472::train::INFO] [train] Iter 590371 | loss 1.4671 | loss(rot) 0.0075 | loss(pos) 1.4590 | loss(seq) 0.0006 | grad 15.2802 | lr 0.0000 | time_forward 4.0030 | time_backward 5.4550 |
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