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[2023-09-02 15:17:46,916::train::INFO] [train] Iter 11275 | loss 1.6839 | loss(rot) 1.3155 | loss(pos) 0.2371 | loss(seq) 0.1312 | grad 6.0285 | lr 0.0010 | time_forward 1.3140 | time_backward 1.4870 |
[2023-09-02 15:17:54,390::train::INFO] [train] Iter 11276 | loss 1.5345 | loss(rot) 1.2985 | loss(pos) 0.1184 | loss(seq) 0.1176 | grad 5.4061 | lr 0.0010 | time_forward 3.0260 | time_backward 4.4440 |
[2023-09-02 15:18:02,414::train::INFO] [train] Iter 11277 | loss 2.5739 | loss(rot) 2.2856 | loss(pos) 0.1456 | loss(seq) 0.1427 | grad 4.7223 | lr 0.0010 | time_forward 3.4310 | time_backward 4.5910 |
[2023-09-02 15:18:11,493::train::INFO] [train] Iter 11278 | loss 0.6600 | loss(rot) 0.1497 | loss(pos) 0.4790 | loss(seq) 0.0313 | grad 5.1642 | lr 0.0010 | time_forward 3.9020 | time_backward 5.1740 |
[2023-09-02 15:18:18,868::train::INFO] [train] Iter 11279 | loss 0.6635 | loss(rot) 0.1163 | loss(pos) 0.3732 | loss(seq) 0.1740 | grad 4.4049 | lr 0.0010 | time_forward 2.8840 | time_backward 4.4880 |
[2023-09-02 15:18:28,596::train::INFO] [train] Iter 11280 | loss 2.0059 | loss(rot) 1.2467 | loss(pos) 0.2981 | loss(seq) 0.4610 | grad 4.0742 | lr 0.0010 | time_forward 3.7050 | time_backward 5.9820 |
[2023-09-02 15:18:36,372::train::INFO] [train] Iter 11281 | loss 1.2669 | loss(rot) 0.8422 | loss(pos) 0.2178 | loss(seq) 0.2068 | grad 4.0094 | lr 0.0010 | time_forward 3.3930 | time_backward 4.3800 |
[2023-09-02 15:18:44,346::train::INFO] [train] Iter 11282 | loss 1.5102 | loss(rot) 0.5475 | loss(pos) 0.5809 | loss(seq) 0.3818 | grad 6.0649 | lr 0.0010 | time_forward 3.3230 | time_backward 4.6470 |
[2023-09-02 15:18:53,808::train::INFO] [train] Iter 11283 | loss 2.4363 | loss(rot) 1.8578 | loss(pos) 0.2310 | loss(seq) 0.3475 | grad 4.5653 | lr 0.0010 | time_forward 3.7150 | time_backward 5.7430 |
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