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[2023-10-22 16:19:30,769::train::INFO] [train] Iter 559493 | loss 0.4337 | loss(rot) 0.1975 | loss(pos) 0.0798 | loss(seq) 0.1564 | grad 3.2917 | lr 0.0000 | time_forward 3.5740 | time_backward 4.6390 |
[2023-10-22 16:19:40,266::train::INFO] [train] Iter 559494 | loss 1.5582 | loss(rot) 1.0817 | loss(pos) 0.3971 | loss(seq) 0.0795 | grad 6.2503 | lr 0.0000 | time_forward 3.8940 | time_backward 5.6010 |
[2023-10-22 16:19:49,860::train::INFO] [train] Iter 559495 | loss 2.0231 | loss(rot) 1.7106 | loss(pos) 0.0973 | loss(seq) 0.2152 | grad 7.0554 | lr 0.0000 | time_forward 3.9720 | time_backward 5.6190 |
[2023-10-22 16:19:52,619::train::INFO] [train] Iter 559496 | loss 1.4803 | loss(rot) 1.0370 | loss(pos) 0.0641 | loss(seq) 0.3792 | grad 3.0870 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4440 |
[2023-10-22 16:20:01,092::train::INFO] [train] Iter 559497 | loss 0.5119 | loss(rot) 0.0381 | loss(pos) 0.4573 | loss(seq) 0.0164 | grad 7.6410 | lr 0.0000 | time_forward 3.5900 | time_backward 4.8470 |
[2023-10-22 16:20:04,499::train::INFO] [train] Iter 559498 | loss 0.7197 | loss(rot) 0.4701 | loss(pos) 0.0384 | loss(seq) 0.2113 | grad 3.8577 | lr 0.0000 | time_forward 1.4890 | time_backward 1.9140 |
[2023-10-22 16:20:07,278::train::INFO] [train] Iter 559499 | loss 0.6223 | loss(rot) 0.0720 | loss(pos) 0.2245 | loss(seq) 0.3259 | grad 4.0090 | lr 0.0000 | time_forward 1.2870 | time_backward 1.4880 |
[2023-10-22 16:20:17,678::train::INFO] [train] Iter 559500 | loss 0.3050 | loss(rot) 0.1178 | loss(pos) 0.0777 | loss(seq) 0.1095 | grad 2.6486 | lr 0.0000 | time_forward 4.1740 | time_backward 6.2230 |
[2023-10-22 16:20:20,794::train::INFO] [train] Iter 559501 | loss 0.8234 | loss(rot) 0.6647 | loss(pos) 0.0256 | loss(seq) 0.1331 | grad 4.0619 | lr 0.0000 | time_forward 1.5430 | time_backward 1.5700 |
[2023-10-22 16:20:23,882::train::INFO] [train] Iter 559502 | loss 0.7860 | loss(rot) 0.3004 | loss(pos) 0.0621 | loss(seq) 0.4235 | grad 3.1942 | lr 0.0000 | time_forward 1.5010 | time_backward 1.5830 |
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