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[2023-10-23 13:33:32,286::train::INFO] [train] Iter 571381 | loss 0.4019 | loss(rot) 0.3428 | loss(pos) 0.0592 | loss(seq) 0.0000 | grad 1.8744 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4430 |
[2023-10-23 13:33:40,385::train::INFO] [train] Iter 571382 | loss 0.9784 | loss(rot) 0.5678 | loss(pos) 0.0364 | loss(seq) 0.3742 | grad 6.8643 | lr 0.0000 | time_forward 3.4300 | time_backward 4.6650 |
[2023-10-23 13:33:49,069::train::INFO] [train] Iter 571383 | loss 0.3006 | loss(rot) 0.1356 | loss(pos) 0.0815 | loss(seq) 0.0834 | grad 2.9671 | lr 0.0000 | time_forward 3.5610 | time_backward 5.1200 |
[2023-10-23 13:33:57,061::train::INFO] [train] Iter 571384 | loss 0.8081 | loss(rot) 0.0139 | loss(pos) 0.7936 | loss(seq) 0.0006 | grad 10.2180 | lr 0.0000 | time_forward 3.3820 | time_backward 4.6060 |
[2023-10-23 13:34:04,870::train::INFO] [train] Iter 571385 | loss 0.9636 | loss(rot) 0.4707 | loss(pos) 0.3731 | loss(seq) 0.1198 | grad 4.5344 | lr 0.0000 | time_forward 3.3620 | time_backward 4.4430 |
[2023-10-23 13:34:12,521::train::INFO] [train] Iter 571386 | loss 0.2411 | loss(rot) 0.2096 | loss(pos) 0.0315 | loss(seq) 0.0000 | grad 20.8787 | lr 0.0000 | time_forward 3.2620 | time_backward 4.3870 |
[2023-10-23 13:34:18,574::train::INFO] [train] Iter 571387 | loss 0.7728 | loss(rot) 0.4655 | loss(pos) 0.0510 | loss(seq) 0.2563 | grad 3.5254 | lr 0.0000 | time_forward 2.6340 | time_backward 3.4150 |
[2023-10-23 13:34:27,046::train::INFO] [train] Iter 571388 | loss 0.5843 | loss(rot) 0.2764 | loss(pos) 0.0343 | loss(seq) 0.2736 | grad 3.3079 | lr 0.0000 | time_forward 3.5030 | time_backward 4.9660 |
[2023-10-23 13:34:35,399::train::INFO] [train] Iter 571389 | loss 0.7823 | loss(rot) 0.5586 | loss(pos) 0.0417 | loss(seq) 0.1820 | grad 3.4867 | lr 0.0000 | time_forward 3.6540 | time_backward 4.6960 |
[2023-10-23 13:34:42,799::train::INFO] [train] Iter 571390 | loss 1.8367 | loss(rot) 1.7317 | loss(pos) 0.0893 | loss(seq) 0.0158 | grad 3.2557 | lr 0.0000 | time_forward 3.1830 | time_backward 4.2150 |
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