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[2023-10-24 18:34:04,191::train::INFO] [train] Iter 586466 | loss 0.5041 | loss(rot) 0.0312 | loss(pos) 0.4598 | loss(seq) 0.0130 | grad 7.0784 | lr 0.0000 | time_forward 1.0650 | time_backward 1.2250 |
[2023-10-24 18:34:10,620::train::INFO] [train] Iter 586467 | loss 1.3983 | loss(rot) 0.1199 | loss(pos) 1.2770 | loss(seq) 0.0015 | grad 13.1260 | lr 0.0000 | time_forward 2.7960 | time_backward 3.6180 |
[2023-10-24 18:34:18,796::train::INFO] [train] Iter 586468 | loss 0.5584 | loss(rot) 0.4784 | loss(pos) 0.0349 | loss(seq) 0.0451 | grad 4.6134 | lr 0.0000 | time_forward 3.5070 | time_backward 4.6660 |
[2023-10-24 18:34:21,038::train::INFO] [train] Iter 586469 | loss 0.2120 | loss(rot) 0.0676 | loss(pos) 0.0769 | loss(seq) 0.0675 | grad 2.1656 | lr 0.0000 | time_forward 1.0360 | time_backward 1.2020 |
[2023-10-24 18:34:30,018::train::INFO] [train] Iter 586470 | loss 0.2218 | loss(rot) 0.0426 | loss(pos) 0.0876 | loss(seq) 0.0916 | grad 2.7139 | lr 0.0000 | time_forward 3.6490 | time_backward 5.3280 |
[2023-10-24 18:34:37,798::train::INFO] [train] Iter 586471 | loss 0.3750 | loss(rot) 0.0477 | loss(pos) 0.3209 | loss(seq) 0.0064 | grad 6.0472 | lr 0.0000 | time_forward 3.3240 | time_backward 4.4530 |
[2023-10-24 18:34:40,487::train::INFO] [train] Iter 586472 | loss 0.4291 | loss(rot) 0.1174 | loss(pos) 0.2958 | loss(seq) 0.0159 | grad 4.4721 | lr 0.0000 | time_forward 1.2900 | time_backward 1.3960 |
[2023-10-24 18:34:48,187::train::INFO] [train] Iter 586473 | loss 0.5045 | loss(rot) 0.1088 | loss(pos) 0.0550 | loss(seq) 0.3408 | grad 3.2863 | lr 0.0000 | time_forward 3.3030 | time_backward 4.3950 |
[2023-10-24 18:34:56,446::train::INFO] [train] Iter 586474 | loss 2.8590 | loss(rot) 2.1491 | loss(pos) 0.1207 | loss(seq) 0.5892 | grad 8.8250 | lr 0.0000 | time_forward 3.1300 | time_backward 5.1250 |
[2023-10-24 18:35:05,583::train::INFO] [train] Iter 586475 | loss 0.2474 | loss(rot) 0.2126 | loss(pos) 0.0347 | loss(seq) 0.0002 | grad 2.9432 | lr 0.0000 | time_forward 4.3710 | time_backward 4.7630 |
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