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[2023-10-25 18:59:16,887::train::INFO] [train] Iter 599253 | loss 1.1108 | loss(rot) 0.7812 | loss(pos) 0.0731 | loss(seq) 0.2564 | grad 5.3774 | lr 0.0000 | time_forward 3.5340 | time_backward 4.5410 |
[2023-10-25 18:59:24,810::train::INFO] [train] Iter 599254 | loss 0.2445 | loss(rot) 0.0534 | loss(pos) 0.1522 | loss(seq) 0.0388 | grad 5.8157 | lr 0.0000 | time_forward 3.3890 | time_backward 4.5300 |
[2023-10-25 18:59:33,541::train::INFO] [train] Iter 599255 | loss 1.3854 | loss(rot) 1.0380 | loss(pos) 0.0989 | loss(seq) 0.2485 | grad 5.6314 | lr 0.0000 | time_forward 3.5770 | time_backward 5.1500 |
[2023-10-25 18:59:37,888::train::INFO] [train] Iter 599256 | loss 0.6366 | loss(rot) 0.6199 | loss(pos) 0.0166 | loss(seq) 0.0002 | grad 5.5646 | lr 0.0000 | time_forward 2.0350 | time_backward 2.3100 |
[2023-10-25 18:59:44,403::train::INFO] [train] Iter 599257 | loss 0.5207 | loss(rot) 0.0885 | loss(pos) 0.0593 | loss(seq) 0.3729 | grad 2.7177 | lr 0.0000 | time_forward 2.7410 | time_backward 3.7700 |
[2023-10-25 18:59:51,393::train::INFO] [train] Iter 599258 | loss 1.7392 | loss(rot) 1.4551 | loss(pos) 0.0327 | loss(seq) 0.2514 | grad 8.8482 | lr 0.0000 | time_forward 2.9070 | time_backward 4.0540 |
[2023-10-25 18:59:58,006::train::INFO] [train] Iter 599259 | loss 0.2647 | loss(rot) 0.2292 | loss(pos) 0.0355 | loss(seq) 0.0000 | grad 2.5436 | lr 0.0000 | time_forward 2.8230 | time_backward 3.7630 |
[2023-10-25 19:00:00,922::train::INFO] [train] Iter 599260 | loss 0.8740 | loss(rot) 0.5795 | loss(pos) 0.0992 | loss(seq) 0.1952 | grad 4.3912 | lr 0.0000 | time_forward 1.4070 | time_backward 1.5060 |
[2023-10-25 19:00:08,743::train::INFO] [train] Iter 599261 | loss 0.2703 | loss(rot) 0.0951 | loss(pos) 0.0222 | loss(seq) 0.1531 | grad 2.3350 | lr 0.0000 | time_forward 3.3630 | time_backward 4.4540 |
[2023-10-25 19:00:16,123::train::INFO] [train] Iter 599262 | loss 1.4117 | loss(rot) 1.1021 | loss(pos) 0.0575 | loss(seq) 0.2521 | grad 4.0203 | lr 0.0000 | time_forward 3.2580 | time_backward 4.1200 |
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