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[2023-09-02 13:27:11,782::train::INFO] [train] Iter 10375 | loss 2.3202 | loss(rot) 2.1764 | loss(pos) 0.1027 | loss(seq) 0.0411 | grad 8.4293 | lr 0.0010 | time_forward 3.5010 | time_backward 4.9170 |
[2023-09-02 13:27:21,808::train::INFO] [train] Iter 10376 | loss 2.9835 | loss(rot) 2.3443 | loss(pos) 0.2555 | loss(seq) 0.3837 | grad 3.3198 | lr 0.0010 | time_forward 4.0250 | time_backward 5.9980 |
[2023-09-02 13:27:31,813::train::INFO] [train] Iter 10377 | loss 1.2634 | loss(rot) 0.7535 | loss(pos) 0.0978 | loss(seq) 0.4121 | grad 6.5523 | lr 0.0010 | time_forward 3.9670 | time_backward 6.0340 |
[2023-09-02 13:27:40,160::train::INFO] [train] Iter 10378 | loss 1.7949 | loss(rot) 1.3008 | loss(pos) 0.2210 | loss(seq) 0.2731 | grad 4.5729 | lr 0.0010 | time_forward 3.4770 | time_backward 4.8670 |
[2023-09-02 13:27:48,460::train::INFO] [train] Iter 10379 | loss 0.9683 | loss(rot) 0.5340 | loss(pos) 0.1628 | loss(seq) 0.2715 | grad 5.1322 | lr 0.0010 | time_forward 3.4520 | time_backward 4.8440 |
[2023-09-02 13:27:51,121::train::INFO] [train] Iter 10380 | loss 1.9923 | loss(rot) 0.7595 | loss(pos) 0.5852 | loss(seq) 0.6476 | grad 5.6196 | lr 0.0010 | time_forward 1.2220 | time_backward 1.4360 |
[2023-09-02 13:27:53,805::train::INFO] [train] Iter 10381 | loss 2.8286 | loss(rot) 2.6258 | loss(pos) 0.1667 | loss(seq) 0.0361 | grad 4.6643 | lr 0.0010 | time_forward 1.2670 | time_backward 1.4120 |
[2023-09-02 13:28:02,319::train::INFO] [train] Iter 10382 | loss 1.5592 | loss(rot) 0.4039 | loss(pos) 0.4615 | loss(seq) 0.6938 | grad 5.1177 | lr 0.0010 | time_forward 3.5820 | time_backward 4.9290 |
[2023-09-02 13:28:11,545::train::INFO] [train] Iter 10383 | loss 0.8755 | loss(rot) 0.2764 | loss(pos) 0.2734 | loss(seq) 0.3256 | grad 3.6511 | lr 0.0010 | time_forward 3.9320 | time_backward 5.2890 |
[2023-09-02 13:28:21,467::train::INFO] [train] Iter 10384 | loss 0.8167 | loss(rot) 0.1115 | loss(pos) 0.5679 | loss(seq) 0.1373 | grad 4.1884 | lr 0.0010 | time_forward 3.9830 | time_backward 5.9350 |
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