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[2023-10-24 12:53:38,372::train::INFO] [train] Iter 583369 | loss 0.3185 | loss(rot) 0.0625 | loss(pos) 0.2401 | loss(seq) 0.0159 | grad 7.0154 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4660 |
[2023-10-24 12:53:47,057::train::INFO] [train] Iter 583370 | loss 0.2226 | loss(rot) 0.1779 | loss(pos) 0.0441 | loss(seq) 0.0006 | grad 15.9203 | lr 0.0000 | time_forward 3.6580 | time_backward 5.0230 |
[2023-10-24 12:53:48,782::train::INFO] [train] Iter 583371 | loss 2.2206 | loss(rot) 1.3241 | loss(pos) 0.3503 | loss(seq) 0.5462 | grad 6.4331 | lr 0.0000 | time_forward 0.7800 | time_backward 0.9410 |
[2023-10-24 12:53:51,688::train::INFO] [train] Iter 583372 | loss 0.2103 | loss(rot) 0.1188 | loss(pos) 0.0196 | loss(seq) 0.0720 | grad 2.0876 | lr 0.0000 | time_forward 1.3300 | time_backward 1.5720 |
[2023-10-24 12:54:01,677::train::INFO] [train] Iter 583373 | loss 0.1457 | loss(rot) 0.1195 | loss(pos) 0.0261 | loss(seq) 0.0001 | grad 2.1962 | lr 0.0000 | time_forward 4.0670 | time_backward 5.9190 |
[2023-10-24 12:54:04,968::train::INFO] [train] Iter 583374 | loss 1.3565 | loss(rot) 0.7381 | loss(pos) 0.2045 | loss(seq) 0.4139 | grad 15.9486 | lr 0.0000 | time_forward 1.4510 | time_backward 1.8360 |
[2023-10-24 12:54:07,750::train::INFO] [train] Iter 583375 | loss 2.3799 | loss(rot) 2.0129 | loss(pos) 0.0902 | loss(seq) 0.2769 | grad 4.3880 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4450 |
[2023-10-24 12:54:15,760::train::INFO] [train] Iter 583376 | loss 0.1452 | loss(rot) 0.0453 | loss(pos) 0.0299 | loss(seq) 0.0700 | grad 1.4705 | lr 0.0000 | time_forward 3.3690 | time_backward 4.6390 |
[2023-10-24 12:54:18,593::train::INFO] [train] Iter 583377 | loss 0.4338 | loss(rot) 0.1468 | loss(pos) 0.0423 | loss(seq) 0.2447 | grad 2.8911 | lr 0.0000 | time_forward 1.3620 | time_backward 1.4680 |
[2023-10-24 12:54:27,713::train::INFO] [train] Iter 583378 | loss 0.1372 | loss(rot) 0.0689 | loss(pos) 0.0242 | loss(seq) 0.0440 | grad 1.5259 | lr 0.0000 | time_forward 3.8500 | time_backward 5.2490 |
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