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[2023-09-02 12:38:53,921::train::INFO] [train] Iter 09976 | loss 1.6574 | loss(rot) 1.5733 | loss(pos) 0.0827 | loss(seq) 0.0013 | grad 4.2469 | lr 0.0010 | time_forward 1.2620 | time_backward 1.5450 |
[2023-09-02 12:39:02,496::train::INFO] [train] Iter 09977 | loss 1.3223 | loss(rot) 0.7568 | loss(pos) 0.1733 | loss(seq) 0.3921 | grad 4.4979 | lr 0.0010 | time_forward 3.5760 | time_backward 4.9960 |
[2023-09-02 12:39:05,218::train::INFO] [train] Iter 09978 | loss 1.5347 | loss(rot) 0.0445 | loss(pos) 1.4858 | loss(seq) 0.0044 | grad 6.4473 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4630 |
[2023-09-02 12:39:13,642::train::INFO] [train] Iter 09979 | loss 1.3799 | loss(rot) 0.9835 | loss(pos) 0.1363 | loss(seq) 0.2600 | grad 4.6007 | lr 0.0010 | time_forward 3.6220 | time_backward 4.7980 |
[2023-09-02 12:39:18,359::train::INFO] [train] Iter 09980 | loss 1.8733 | loss(rot) 1.0299 | loss(pos) 0.4525 | loss(seq) 0.3909 | grad 5.6839 | lr 0.0010 | time_forward 2.0520 | time_backward 2.6610 |
[2023-09-02 12:39:20,791::train::INFO] [train] Iter 09981 | loss 1.5197 | loss(rot) 0.8718 | loss(pos) 0.2201 | loss(seq) 0.4278 | grad 5.4575 | lr 0.0010 | time_forward 1.1030 | time_backward 1.3260 |
[2023-09-02 12:39:30,800::train::INFO] [train] Iter 09982 | loss 1.8372 | loss(rot) 1.6218 | loss(pos) 0.2086 | loss(seq) 0.0069 | grad 4.8807 | lr 0.0010 | time_forward 4.0570 | time_backward 5.9200 |
[2023-09-02 12:39:39,431::train::INFO] [train] Iter 09983 | loss 0.5244 | loss(rot) 0.1830 | loss(pos) 0.2871 | loss(seq) 0.0544 | grad 2.5130 | lr 0.0010 | time_forward 3.7310 | time_backward 4.8960 |
[2023-09-02 12:39:49,314::train::INFO] [train] Iter 09984 | loss 0.8690 | loss(rot) 0.3118 | loss(pos) 0.2326 | loss(seq) 0.3246 | grad 3.4131 | lr 0.0010 | time_forward 4.0210 | time_backward 5.8580 |
[2023-09-02 12:39:58,464::train::INFO] [train] Iter 09985 | loss 1.4594 | loss(rot) 0.9753 | loss(pos) 0.1228 | loss(seq) 0.3614 | grad 6.7898 | lr 0.0010 | time_forward 3.8960 | time_backward 5.2510 |
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