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[2023-10-24 00:23:39,119::train::INFO] [train] Iter 577074 | loss 0.2922 | loss(rot) 0.0327 | loss(pos) 0.1766 | loss(seq) 0.0830 | grad 4.8676 | lr 0.0000 | time_forward 3.0840 | time_backward 4.3990 |
[2023-10-24 00:23:41,382::train::INFO] [train] Iter 577075 | loss 0.9117 | loss(rot) 0.8493 | loss(pos) 0.0517 | loss(seq) 0.0107 | grad 5.3953 | lr 0.0000 | time_forward 1.0360 | time_backward 1.2250 |
[2023-10-24 00:23:51,466::train::INFO] [train] Iter 577076 | loss 0.4834 | loss(rot) 0.1408 | loss(pos) 0.1562 | loss(seq) 0.1864 | grad 3.0916 | lr 0.0000 | time_forward 4.1650 | time_backward 5.9160 |
[2023-10-24 00:24:00,685::train::INFO] [train] Iter 577077 | loss 0.2274 | loss(rot) 0.1585 | loss(pos) 0.0341 | loss(seq) 0.0348 | grad 1.9687 | lr 0.0000 | time_forward 3.8210 | time_backward 5.3950 |
[2023-10-24 00:24:03,461::train::INFO] [train] Iter 577078 | loss 1.1354 | loss(rot) 0.6735 | loss(pos) 0.1948 | loss(seq) 0.2672 | grad 7.8964 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4670 |
[2023-10-24 00:24:05,976::train::INFO] [train] Iter 577079 | loss 2.0845 | loss(rot) 1.6268 | loss(pos) 0.0833 | loss(seq) 0.3744 | grad 4.5141 | lr 0.0000 | time_forward 1.2530 | time_backward 1.2590 |
[2023-10-24 00:24:09,116::train::INFO] [train] Iter 577080 | loss 0.3281 | loss(rot) 0.1475 | loss(pos) 0.0267 | loss(seq) 0.1539 | grad 1.9436 | lr 0.0000 | time_forward 1.3660 | time_backward 1.7700 |
[2023-10-24 00:24:17,314::train::INFO] [train] Iter 577081 | loss 1.3875 | loss(rot) 1.1215 | loss(pos) 0.0771 | loss(seq) 0.1888 | grad 6.1388 | lr 0.0000 | time_forward 3.6960 | time_backward 4.4900 |
[2023-10-24 00:24:20,158::train::INFO] [train] Iter 577082 | loss 0.6329 | loss(rot) 0.5933 | loss(pos) 0.0217 | loss(seq) 0.0179 | grad 3.9832 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4960 |
[2023-10-24 00:24:26,114::train::INFO] [train] Iter 577083 | loss 0.3973 | loss(rot) 0.0557 | loss(pos) 0.3336 | loss(seq) 0.0080 | grad 3.9692 | lr 0.0000 | time_forward 2.5750 | time_backward 3.3770 |
[2023-10-24 00:24:32,982::train::INFO] [train] Iter 577084 | loss 0.3177 | loss(rot) 0.2774 | loss(pos) 0.0281 | loss(seq) 0.0122 | grad 2.8163 | lr 0.0000 | time_forward 2.9250 | time_backward 3.9240 |
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