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[2023-10-25 04:04:32,893::train::INFO] [train] Iter 591361 | loss 0.8013 | loss(rot) 0.3060 | loss(pos) 0.0778 | loss(seq) 0.4175 | grad 4.0204 | lr 0.0000 | time_forward 3.3650 | time_backward 4.5300 |
[2023-10-25 04:04:35,616::train::INFO] [train] Iter 591362 | loss 0.3666 | loss(rot) 0.3014 | loss(pos) 0.0233 | loss(seq) 0.0419 | grad 1.9366 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4230 |
[2023-10-25 04:04:38,290::train::INFO] [train] Iter 591363 | loss 0.5291 | loss(rot) 0.0448 | loss(pos) 0.4813 | loss(seq) 0.0029 | grad 10.2144 | lr 0.0000 | time_forward 1.2680 | time_backward 1.3750 |
[2023-10-25 04:04:45,971::train::INFO] [train] Iter 591364 | loss 0.3639 | loss(rot) 0.0281 | loss(pos) 0.3305 | loss(seq) 0.0052 | grad 6.6263 | lr 0.0000 | time_forward 3.3170 | time_backward 4.3390 |
[2023-10-25 04:04:53,683::train::INFO] [train] Iter 591365 | loss 0.3756 | loss(rot) 0.0223 | loss(pos) 0.3486 | loss(seq) 0.0046 | grad 7.0094 | lr 0.0000 | time_forward 3.2090 | time_backward 4.4990 |
[2023-10-25 04:05:01,530::train::INFO] [train] Iter 591366 | loss 0.7950 | loss(rot) 0.0710 | loss(pos) 0.1620 | loss(seq) 0.5619 | grad 4.1747 | lr 0.0000 | time_forward 3.4070 | time_backward 4.4360 |
[2023-10-25 04:05:09,580::train::INFO] [train] Iter 591367 | loss 0.3868 | loss(rot) 0.3190 | loss(pos) 0.0225 | loss(seq) 0.0453 | grad 2.9110 | lr 0.0000 | time_forward 3.4110 | time_backward 4.6350 |
[2023-10-25 04:05:11,805::train::INFO] [train] Iter 591368 | loss 1.6676 | loss(rot) 1.3827 | loss(pos) 0.0383 | loss(seq) 0.2465 | grad 3.9173 | lr 0.0000 | time_forward 1.0260 | time_backward 1.1950 |
[2023-10-25 04:05:20,658::train::INFO] [train] Iter 591369 | loss 1.7404 | loss(rot) 1.2548 | loss(pos) 0.0805 | loss(seq) 0.4051 | grad 5.1924 | lr 0.0000 | time_forward 3.6550 | time_backward 5.1940 |
[2023-10-25 04:05:23,368::train::INFO] [train] Iter 591370 | loss 0.2854 | loss(rot) 0.0326 | loss(pos) 0.2412 | loss(seq) 0.0116 | grad 4.1950 | lr 0.0000 | time_forward 1.2690 | time_backward 1.4380 |
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