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[2023-09-02 22:49:56,530::train::INFO] [train] Iter 15071 | loss 1.9605 | loss(rot) 1.2847 | loss(pos) 0.2032 | loss(seq) 0.4726 | grad 3.3633 | lr 0.0010 | time_forward 4.2900 | time_backward 6.5040 |
[2023-09-02 22:49:59,472::train::INFO] [train] Iter 15072 | loss 0.5630 | loss(rot) 0.2196 | loss(pos) 0.3142 | loss(seq) 0.0292 | grad 3.5680 | lr 0.0010 | time_forward 1.3580 | time_backward 1.5800 |
[2023-09-02 22:50:10,341::train::INFO] [train] Iter 15073 | loss 1.2443 | loss(rot) 0.5095 | loss(pos) 0.3330 | loss(seq) 0.4017 | grad 3.1691 | lr 0.0010 | time_forward 4.3240 | time_backward 6.5410 |
[2023-09-02 22:50:13,218::train::INFO] [train] Iter 15074 | loss 0.7295 | loss(rot) 0.1105 | loss(pos) 0.5935 | loss(seq) 0.0255 | grad 4.3197 | lr 0.0010 | time_forward 1.3160 | time_backward 1.5570 |
[2023-09-02 22:50:24,087::train::INFO] [train] Iter 15075 | loss 0.2891 | loss(rot) 0.1135 | loss(pos) 0.1390 | loss(seq) 0.0366 | grad 2.4157 | lr 0.0010 | time_forward 4.2780 | time_backward 6.5880 |
[2023-09-02 22:50:35,254::train::INFO] [train] Iter 15076 | loss 0.7504 | loss(rot) 0.1324 | loss(pos) 0.5989 | loss(seq) 0.0191 | grad 4.2353 | lr 0.0010 | time_forward 4.7030 | time_backward 6.4620 |
[2023-09-02 22:50:44,600::train::INFO] [train] Iter 15077 | loss 0.8920 | loss(rot) 0.7156 | loss(pos) 0.1716 | loss(seq) 0.0048 | grad 4.5097 | lr 0.0010 | time_forward 4.0390 | time_backward 5.3030 |
[2023-09-02 22:50:47,410::train::INFO] [train] Iter 15078 | loss 2.6378 | loss(rot) 1.7958 | loss(pos) 0.3455 | loss(seq) 0.4965 | grad 2.9226 | lr 0.0010 | time_forward 1.3410 | time_backward 1.4640 |
[2023-09-02 22:50:58,388::train::INFO] [train] Iter 15079 | loss 1.0475 | loss(rot) 0.3338 | loss(pos) 0.5283 | loss(seq) 0.1854 | grad 3.4380 | lr 0.0010 | time_forward 4.4340 | time_backward 6.5400 |
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