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[2023-09-02 10:24:25,669::train::INFO] [train] Iter 08877 | loss 1.1274 | loss(rot) 0.8875 | loss(pos) 0.0872 | loss(seq) 0.1528 | grad 4.8585 | lr 0.0010 | time_forward 3.5540 | time_backward 4.7010 |
[2023-09-02 10:24:31,667::train::INFO] [train] Iter 08878 | loss 1.4928 | loss(rot) 0.9559 | loss(pos) 0.1639 | loss(seq) 0.3730 | grad 4.2816 | lr 0.0010 | time_forward 2.5150 | time_backward 3.4790 |
[2023-09-02 10:24:41,887::train::INFO] [train] Iter 08879 | loss 1.7215 | loss(rot) 0.6946 | loss(pos) 0.4656 | loss(seq) 0.5613 | grad 5.5765 | lr 0.0010 | time_forward 4.2380 | time_backward 5.9790 |
[2023-09-02 10:24:52,012::train::INFO] [train] Iter 08880 | loss 2.0351 | loss(rot) 1.0843 | loss(pos) 0.3954 | loss(seq) 0.5554 | grad 5.7185 | lr 0.0010 | time_forward 4.1360 | time_backward 5.9850 |
[2023-09-02 10:24:54,544::train::INFO] [train] Iter 08881 | loss 2.9363 | loss(rot) 0.0056 | loss(pos) 2.9302 | loss(seq) 0.0005 | grad 6.1940 | lr 0.0010 | time_forward 1.2470 | time_backward 1.2810 |
[2023-09-02 10:25:02,785::train::INFO] [train] Iter 08882 | loss 1.5912 | loss(rot) 0.7541 | loss(pos) 0.1469 | loss(seq) 0.6903 | grad 5.0112 | lr 0.0010 | time_forward 3.4650 | time_backward 4.7530 |
[2023-09-02 10:25:05,307::train::INFO] [train] Iter 08883 | loss 2.1006 | loss(rot) 0.0545 | loss(pos) 2.0285 | loss(seq) 0.0176 | grad 7.9737 | lr 0.0010 | time_forward 1.2010 | time_backward 1.3170 |
[2023-09-02 10:25:15,234::train::INFO] [train] Iter 08884 | loss 1.5663 | loss(rot) 0.9298 | loss(pos) 0.2141 | loss(seq) 0.4224 | grad 5.7453 | lr 0.0010 | time_forward 4.0920 | time_backward 5.8100 |
[2023-09-02 10:25:23,491::train::INFO] [train] Iter 08885 | loss 0.9774 | loss(rot) 0.8007 | loss(pos) 0.1017 | loss(seq) 0.0750 | grad 4.7273 | lr 0.0010 | time_forward 3.5270 | time_backward 4.7270 |
[2023-09-02 10:25:31,763::train::INFO] [train] Iter 08886 | loss 1.2073 | loss(rot) 0.0260 | loss(pos) 1.1730 | loss(seq) 0.0082 | grad 6.3622 | lr 0.0010 | time_forward 3.5180 | time_backward 4.7500 |
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