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[2023-10-24 13:29:21,524::train::INFO] [train] Iter 583669 | loss 0.3732 | loss(rot) 0.3386 | loss(pos) 0.0245 | loss(seq) 0.0100 | grad 3.6086 | lr 0.0000 | time_forward 3.7400 | time_backward 5.1780 |
[2023-10-24 13:29:24,335::train::INFO] [train] Iter 583670 | loss 0.4935 | loss(rot) 0.1366 | loss(pos) 0.0521 | loss(seq) 0.3048 | grad 2.9455 | lr 0.0000 | time_forward 1.3440 | time_backward 1.4650 |
[2023-10-24 13:29:34,448::train::INFO] [train] Iter 583671 | loss 0.4502 | loss(rot) 0.2383 | loss(pos) 0.0308 | loss(seq) 0.1811 | grad 3.3262 | lr 0.0000 | time_forward 4.1580 | time_backward 5.9510 |
[2023-10-24 13:29:42,875::train::INFO] [train] Iter 583672 | loss 2.7129 | loss(rot) 0.0039 | loss(pos) 2.7090 | loss(seq) 0.0000 | grad 25.4148 | lr 0.0000 | time_forward 3.5370 | time_backward 4.8880 |
[2023-10-24 13:29:49,475::train::INFO] [train] Iter 583673 | loss 0.8519 | loss(rot) 0.1867 | loss(pos) 0.0786 | loss(seq) 0.5866 | grad 2.8641 | lr 0.0000 | time_forward 2.8080 | time_backward 3.7880 |
[2023-10-24 13:29:57,799::train::INFO] [train] Iter 583674 | loss 1.2800 | loss(rot) 0.8401 | loss(pos) 0.0519 | loss(seq) 0.3879 | grad 4.2167 | lr 0.0000 | time_forward 3.4880 | time_backward 4.8180 |
[2023-10-24 13:30:07,798::train::INFO] [train] Iter 583675 | loss 1.0172 | loss(rot) 0.6729 | loss(pos) 0.0374 | loss(seq) 0.3069 | grad 3.7941 | lr 0.0000 | time_forward 4.0170 | time_backward 5.9790 |
[2023-10-24 13:30:15,351::train::INFO] [train] Iter 583676 | loss 0.4741 | loss(rot) 0.0955 | loss(pos) 0.0655 | loss(seq) 0.3130 | grad 3.1707 | lr 0.0000 | time_forward 3.1750 | time_backward 4.3750 |
[2023-10-24 13:30:25,355::train::INFO] [train] Iter 583677 | loss 1.2134 | loss(rot) 1.1188 | loss(pos) 0.0444 | loss(seq) 0.0502 | grad 3.9947 | lr 0.0000 | time_forward 4.1060 | time_backward 5.8950 |
[2023-10-24 13:30:33,692::train::INFO] [train] Iter 583678 | loss 0.8561 | loss(rot) 0.1926 | loss(pos) 0.6600 | loss(seq) 0.0035 | grad 6.9363 | lr 0.0000 | time_forward 3.4730 | time_backward 4.8620 |
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