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[2023-10-24 17:09:14,122::train::INFO] [train] Iter 585667 | loss 0.6948 | loss(rot) 0.0495 | loss(pos) 0.5923 | loss(seq) 0.0529 | grad 5.6182 | lr 0.0000 | time_forward 3.5360 | time_backward 5.0740 |
[2023-10-24 17:09:22,906::train::INFO] [train] Iter 585668 | loss 1.0548 | loss(rot) 0.5389 | loss(pos) 0.1652 | loss(seq) 0.3507 | grad 3.8686 | lr 0.0000 | time_forward 3.5820 | time_backward 5.1990 |
[2023-10-24 17:09:25,739::train::INFO] [train] Iter 585669 | loss 0.5815 | loss(rot) 0.0581 | loss(pos) 0.5191 | loss(seq) 0.0043 | grad 6.5688 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4720 |
[2023-10-24 17:09:33,455::train::INFO] [train] Iter 585670 | loss 1.3920 | loss(rot) 0.9302 | loss(pos) 0.0951 | loss(seq) 0.3667 | grad 8.3889 | lr 0.0000 | time_forward 3.3060 | time_backward 4.3940 |
[2023-10-24 17:09:36,297::train::INFO] [train] Iter 585671 | loss 0.1857 | loss(rot) 0.0457 | loss(pos) 0.0477 | loss(seq) 0.0923 | grad 2.7947 | lr 0.0000 | time_forward 1.3920 | time_backward 1.4460 |
[2023-10-24 17:09:45,151::train::INFO] [train] Iter 585672 | loss 1.0246 | loss(rot) 0.3765 | loss(pos) 0.4919 | loss(seq) 0.1561 | grad 4.0775 | lr 0.0000 | time_forward 3.6780 | time_backward 5.1720 |
[2023-10-24 17:09:50,697::train::INFO] [train] Iter 585673 | loss 0.5705 | loss(rot) 0.3210 | loss(pos) 0.0383 | loss(seq) 0.2112 | grad 3.1198 | lr 0.0000 | time_forward 2.4040 | time_backward 3.1390 |
[2023-10-24 17:09:57,436::train::INFO] [train] Iter 585674 | loss 0.2339 | loss(rot) 0.1394 | loss(pos) 0.0772 | loss(seq) 0.0172 | grad 3.2922 | lr 0.0000 | time_forward 2.9100 | time_backward 3.8260 |
[2023-10-24 17:10:05,021::train::INFO] [train] Iter 585675 | loss 0.5192 | loss(rot) 0.0995 | loss(pos) 0.3307 | loss(seq) 0.0890 | grad 6.3307 | lr 0.0000 | time_forward 3.3000 | time_backward 4.2810 |
[2023-10-24 17:10:08,184::train::INFO] [train] Iter 585676 | loss 0.8328 | loss(rot) 0.5377 | loss(pos) 0.0562 | loss(seq) 0.2389 | grad 3.0269 | lr 0.0000 | time_forward 1.4580 | time_backward 1.7010 |
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