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[2023-09-02 15:41:11,867::train::INFO] [train] Iter 11474 | loss 2.2346 | loss(rot) 1.3267 | loss(pos) 0.3200 | loss(seq) 0.5880 | grad 4.4538 | lr 0.0010 | time_forward 3.2910 | time_backward 4.3180 |
[2023-09-02 15:41:19,834::train::INFO] [train] Iter 11475 | loss 1.4529 | loss(rot) 1.0066 | loss(pos) 0.1787 | loss(seq) 0.2677 | grad 4.4948 | lr 0.0010 | time_forward 3.4520 | time_backward 4.5110 |
[2023-09-02 15:41:27,238::train::INFO] [train] Iter 11476 | loss 1.0924 | loss(rot) 0.4451 | loss(pos) 0.1266 | loss(seq) 0.5207 | grad 4.4470 | lr 0.0010 | time_forward 3.1910 | time_backward 4.2100 |
[2023-09-02 15:41:29,857::train::INFO] [train] Iter 11477 | loss 1.8713 | loss(rot) 1.3151 | loss(pos) 0.1721 | loss(seq) 0.3841 | grad 5.0885 | lr 0.0010 | time_forward 1.2320 | time_backward 1.3830 |
[2023-09-02 15:41:37,468::train::INFO] [train] Iter 11478 | loss 0.9035 | loss(rot) 0.7155 | loss(pos) 0.0968 | loss(seq) 0.0912 | grad 3.6002 | lr 0.0010 | time_forward 3.3190 | time_backward 4.2890 |
[2023-09-02 15:41:45,462::train::INFO] [train] Iter 11479 | loss 0.7848 | loss(rot) 0.5443 | loss(pos) 0.1003 | loss(seq) 0.1402 | grad 2.3761 | lr 0.0010 | time_forward 3.4120 | time_backward 4.5780 |
[2023-09-02 15:41:54,437::train::INFO] [train] Iter 11480 | loss 2.2962 | loss(rot) 1.6439 | loss(pos) 0.1888 | loss(seq) 0.4636 | grad 5.2433 | lr 0.0010 | time_forward 3.8080 | time_backward 5.1630 |
[2023-09-02 15:41:57,063::train::INFO] [train] Iter 11481 | loss 0.9156 | loss(rot) 0.1501 | loss(pos) 0.7188 | loss(seq) 0.0468 | grad 5.2327 | lr 0.0010 | time_forward 1.2260 | time_backward 1.3970 |
[2023-09-02 15:42:04,771::train::INFO] [train] Iter 11482 | loss 1.0888 | loss(rot) 0.4208 | loss(pos) 0.2005 | loss(seq) 0.4676 | grad 4.5284 | lr 0.0010 | time_forward 3.2140 | time_backward 4.4900 |
[2023-09-02 15:42:11,308::train::INFO] [train] Iter 11483 | loss 1.0791 | loss(rot) 0.8614 | loss(pos) 0.2052 | loss(seq) 0.0124 | grad 5.0904 | lr 0.0010 | time_forward 2.8540 | time_backward 3.6780 |
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