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[2023-09-02 11:01:01,665::train::INFO] [train] Iter 09176 | loss 1.1290 | loss(rot) 0.1615 | loss(pos) 0.9410 | loss(seq) 0.0266 | grad 4.2814 | lr 0.0010 | time_forward 4.6910 | time_backward 6.2830 |
[2023-09-02 11:01:11,366::train::INFO] [train] Iter 09177 | loss 1.7602 | loss(rot) 0.9051 | loss(pos) 0.4474 | loss(seq) 0.4078 | grad 3.4026 | lr 0.0010 | time_forward 4.1280 | time_backward 5.5690 |
[2023-09-02 11:01:14,109::train::INFO] [train] Iter 09178 | loss 2.0724 | loss(rot) 1.9704 | loss(pos) 0.1008 | loss(seq) 0.0012 | grad 5.4026 | lr 0.0010 | time_forward 1.2810 | time_backward 1.4480 |
[2023-09-02 11:01:22,922::train::INFO] [train] Iter 09179 | loss 1.8100 | loss(rot) 1.6056 | loss(pos) 0.1453 | loss(seq) 0.0590 | grad 4.7590 | lr 0.0010 | time_forward 3.7840 | time_backward 5.0260 |
[2023-09-02 11:01:31,321::train::INFO] [train] Iter 09180 | loss 3.2155 | loss(rot) 2.1373 | loss(pos) 0.5224 | loss(seq) 0.5559 | grad 3.6099 | lr 0.0010 | time_forward 3.4230 | time_backward 4.9730 |
[2023-09-02 11:01:41,562::train::INFO] [train] Iter 09181 | loss 1.6566 | loss(rot) 0.4437 | loss(pos) 0.7278 | loss(seq) 0.4851 | grad 5.9429 | lr 0.0010 | time_forward 4.1080 | time_backward 6.1290 |
[2023-09-02 11:01:44,324::train::INFO] [train] Iter 09182 | loss 1.8585 | loss(rot) 1.1372 | loss(pos) 0.1795 | loss(seq) 0.5419 | grad 3.9439 | lr 0.0010 | time_forward 1.3070 | time_backward 1.4510 |
[2023-09-02 11:01:53,879::train::INFO] [train] Iter 09183 | loss 2.6548 | loss(rot) 2.4377 | loss(pos) 0.2152 | loss(seq) 0.0019 | grad 5.8239 | lr 0.0010 | time_forward 4.5430 | time_backward 4.9940 |
[2023-09-02 11:02:02,036::train::INFO] [train] Iter 09184 | loss 0.9297 | loss(rot) 0.2440 | loss(pos) 0.4605 | loss(seq) 0.2253 | grad 4.0395 | lr 0.0010 | time_forward 3.3720 | time_backward 4.7810 |
[2023-09-02 11:02:04,900::train::INFO] [train] Iter 09185 | loss 2.1074 | loss(rot) 0.0295 | loss(pos) 2.0775 | loss(seq) 0.0005 | grad 4.4361 | lr 0.0010 | time_forward 1.3310 | time_backward 1.5290 |
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