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[2023-10-23 20:14:47,945::train::INFO] [train] Iter 574978 | loss 0.3887 | loss(rot) 0.1603 | loss(pos) 0.0618 | loss(seq) 0.1666 | grad 3.2559 | lr 0.0000 | time_forward 3.2990 | time_backward 4.3530 |
[2023-10-23 20:14:52,422::train::INFO] [train] Iter 574979 | loss 0.2731 | loss(rot) 0.0931 | loss(pos) 0.0357 | loss(seq) 0.1443 | grad 2.6158 | lr 0.0000 | time_forward 2.0860 | time_backward 2.3890 |
[2023-10-23 20:15:00,272::train::INFO] [train] Iter 574980 | loss 1.1218 | loss(rot) 0.6056 | loss(pos) 0.1707 | loss(seq) 0.3455 | grad 2.3624 | lr 0.0000 | time_forward 3.4400 | time_backward 4.4050 |
[2023-10-23 20:15:08,604::train::INFO] [train] Iter 574981 | loss 0.3479 | loss(rot) 0.2988 | loss(pos) 0.0487 | loss(seq) 0.0005 | grad 3.2583 | lr 0.0000 | time_forward 3.6800 | time_backward 4.6490 |
[2023-10-23 20:15:16,861::train::INFO] [train] Iter 574982 | loss 2.0538 | loss(rot) 1.4732 | loss(pos) 0.2473 | loss(seq) 0.3332 | grad 32.8456 | lr 0.0000 | time_forward 3.5170 | time_backward 4.7380 |
[2023-10-23 20:15:19,401::train::INFO] [train] Iter 574983 | loss 0.4986 | loss(rot) 0.1920 | loss(pos) 0.0513 | loss(seq) 0.2553 | grad 3.6012 | lr 0.0000 | time_forward 1.2300 | time_backward 1.3060 |
[2023-10-23 20:15:22,194::train::INFO] [train] Iter 574984 | loss 1.3964 | loss(rot) 1.2277 | loss(pos) 0.0498 | loss(seq) 0.1189 | grad 3.1821 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4180 |
[2023-10-23 20:15:29,787::train::INFO] [train] Iter 574985 | loss 0.2535 | loss(rot) 0.0689 | loss(pos) 0.0175 | loss(seq) 0.1671 | grad 2.3692 | lr 0.0000 | time_forward 3.2740 | time_backward 4.2810 |
[2023-10-23 20:15:32,620::train::INFO] [train] Iter 574986 | loss 0.4063 | loss(rot) 0.3257 | loss(pos) 0.0177 | loss(seq) 0.0629 | grad 2.5268 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4860 |
[2023-10-23 20:15:40,296::train::INFO] [train] Iter 574987 | loss 0.6813 | loss(rot) 0.2188 | loss(pos) 0.4616 | loss(seq) 0.0009 | grad 7.2891 | lr 0.0000 | time_forward 3.2870 | time_backward 4.3850 |
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