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[2023-10-25 14:12:15,055::train::INFO] [train] Iter 596656 | loss 2.4804 | loss(rot) 2.1105 | loss(pos) 0.1938 | loss(seq) 0.1761 | grad 7.2864 | lr 0.0000 | time_forward 0.9650 | time_backward 0.8540 |
[2023-10-25 14:12:25,094::train::INFO] [train] Iter 596657 | loss 1.4171 | loss(rot) 1.2595 | loss(pos) 0.0418 | loss(seq) 0.1159 | grad 4.3366 | lr 0.0000 | time_forward 4.2370 | time_backward 5.7980 |
[2023-10-25 14:12:33,675::train::INFO] [train] Iter 596658 | loss 0.8081 | loss(rot) 0.7581 | loss(pos) 0.0500 | loss(seq) 0.0000 | grad 3.4502 | lr 0.0000 | time_forward 3.5080 | time_backward 5.0700 |
[2023-10-25 14:12:42,459::train::INFO] [train] Iter 596659 | loss 0.9961 | loss(rot) 0.6873 | loss(pos) 0.0580 | loss(seq) 0.2508 | grad 7.4230 | lr 0.0000 | time_forward 3.7800 | time_backward 5.0020 |
[2023-10-25 14:12:47,062::train::INFO] [train] Iter 596660 | loss 0.5834 | loss(rot) 0.1120 | loss(pos) 0.0420 | loss(seq) 0.4294 | grad 6.6405 | lr 0.0000 | time_forward 2.1360 | time_backward 2.4630 |
[2023-10-25 14:12:56,182::train::INFO] [train] Iter 596661 | loss 0.7889 | loss(rot) 0.5405 | loss(pos) 0.0269 | loss(seq) 0.2214 | grad 3.3354 | lr 0.0000 | time_forward 3.6710 | time_backward 5.4450 |
[2023-10-25 14:12:58,578::train::INFO] [train] Iter 596662 | loss 0.6912 | loss(rot) 0.0549 | loss(pos) 0.5260 | loss(seq) 0.1103 | grad 4.3785 | lr 0.0000 | time_forward 1.1400 | time_backward 1.2540 |
[2023-10-25 14:13:08,571::train::INFO] [train] Iter 596663 | loss 0.7019 | loss(rot) 0.6043 | loss(pos) 0.0280 | loss(seq) 0.0697 | grad 3.0628 | lr 0.0000 | time_forward 4.0550 | time_backward 5.9340 |
[2023-10-25 14:13:15,289::train::INFO] [train] Iter 596664 | loss 0.5045 | loss(rot) 0.1471 | loss(pos) 0.3015 | loss(seq) 0.0559 | grad 4.4337 | lr 0.0000 | time_forward 2.9490 | time_backward 3.7660 |
[2023-10-25 14:13:25,411::train::INFO] [train] Iter 596665 | loss 2.0000 | loss(rot) 1.3150 | loss(pos) 0.1468 | loss(seq) 0.5383 | grad 3.6803 | lr 0.0000 | time_forward 4.1880 | time_backward 5.9320 |
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