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[2023-10-23 06:32:11,797::train::INFO] [train] Iter 567485 | loss 1.2480 | loss(rot) 0.9585 | loss(pos) 0.0207 | loss(seq) 0.2689 | grad 20.0812 | lr 0.0000 | time_forward 3.7510 | time_backward 4.6870 |
[2023-10-23 06:32:20,267::train::INFO] [train] Iter 567486 | loss 0.3241 | loss(rot) 0.1122 | loss(pos) 0.0182 | loss(seq) 0.1937 | grad 1.4087 | lr 0.0000 | time_forward 4.1330 | time_backward 4.3330 |
[2023-10-23 06:32:28,990::train::INFO] [train] Iter 567487 | loss 0.6019 | loss(rot) 0.4334 | loss(pos) 0.0254 | loss(seq) 0.1432 | grad 2.7617 | lr 0.0000 | time_forward 3.8620 | time_backward 4.8580 |
[2023-10-23 06:32:38,089::train::INFO] [train] Iter 567488 | loss 0.6710 | loss(rot) 0.0136 | loss(pos) 0.6549 | loss(seq) 0.0025 | grad 12.8577 | lr 0.0000 | time_forward 3.9650 | time_backward 5.1300 |
[2023-10-23 06:32:46,671::train::INFO] [train] Iter 567489 | loss 0.4016 | loss(rot) 0.2224 | loss(pos) 0.0459 | loss(seq) 0.1333 | grad 2.8518 | lr 0.0000 | time_forward 3.8240 | time_backward 4.7550 |
[2023-10-23 06:32:54,573::train::INFO] [train] Iter 567490 | loss 0.4691 | loss(rot) 0.1262 | loss(pos) 0.0453 | loss(seq) 0.2976 | grad 3.0731 | lr 0.0000 | time_forward 3.2340 | time_backward 4.6640 |
[2023-10-23 06:33:01,370::train::INFO] [train] Iter 567491 | loss 0.2600 | loss(rot) 0.0644 | loss(pos) 0.1682 | loss(seq) 0.0274 | grad 4.8789 | lr 0.0000 | time_forward 2.9140 | time_backward 3.8800 |
[2023-10-23 06:33:08,029::train::INFO] [train] Iter 567492 | loss 0.4721 | loss(rot) 0.0574 | loss(pos) 0.1408 | loss(seq) 0.2740 | grad 2.8764 | lr 0.0000 | time_forward 2.8300 | time_backward 3.8270 |
[2023-10-23 06:33:13,928::train::INFO] [train] Iter 567493 | loss 0.1936 | loss(rot) 0.0637 | loss(pos) 0.0690 | loss(seq) 0.0609 | grad 3.3452 | lr 0.0000 | time_forward 2.5650 | time_backward 3.3310 |
[2023-10-23 06:33:21,417::train::INFO] [train] Iter 567494 | loss 2.0965 | loss(rot) 1.8390 | loss(pos) 0.0420 | loss(seq) 0.2156 | grad 3.9924 | lr 0.0000 | time_forward 3.3060 | time_backward 4.1800 |
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