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[2023-09-02 02:04:28,244::train::INFO] [train] Iter 04781 | loss 0.7940 | loss(rot) 0.2150 | loss(pos) 0.5430 | loss(seq) 0.0359 | grad 3.5460 | lr 0.0010 | time_forward 1.2580 | time_backward 1.4020 |
[2023-09-02 02:04:36,871::train::INFO] [train] Iter 04782 | loss 1.4362 | loss(rot) 0.6130 | loss(pos) 0.3925 | loss(seq) 0.4307 | grad 3.7899 | lr 0.0010 | time_forward 3.6150 | time_backward 5.0000 |
[2023-09-02 02:04:46,265::train::INFO] [train] Iter 04783 | loss 0.7814 | loss(rot) 0.2319 | loss(pos) 0.3255 | loss(seq) 0.2240 | grad 2.6399 | lr 0.0010 | time_forward 3.8380 | time_backward 5.5520 |
[2023-09-02 02:04:55,710::train::INFO] [train] Iter 04784 | loss 2.0550 | loss(rot) 1.6579 | loss(pos) 0.1790 | loss(seq) 0.2180 | grad 4.8166 | lr 0.0010 | time_forward 3.7060 | time_backward 5.7360 |
[2023-09-02 02:04:58,375::train::INFO] [train] Iter 04785 | loss 2.0861 | loss(rot) 1.8695 | loss(pos) 0.1958 | loss(seq) 0.0207 | grad 5.8209 | lr 0.0010 | time_forward 1.2340 | time_backward 1.4260 |
[2023-09-02 02:05:00,653::train::INFO] [train] Iter 04786 | loss 2.6372 | loss(rot) 2.4147 | loss(pos) 0.2223 | loss(seq) 0.0001 | grad 3.1572 | lr 0.0010 | time_forward 1.0840 | time_backward 1.1910 |
[2023-09-02 02:05:09,850::train::INFO] [train] Iter 04787 | loss 1.4501 | loss(rot) 0.1446 | loss(pos) 1.2776 | loss(seq) 0.0279 | grad 4.5715 | lr 0.0010 | time_forward 4.0190 | time_backward 5.1740 |
[2023-09-02 02:05:18,199::train::INFO] [train] Iter 04788 | loss 2.9055 | loss(rot) 2.7560 | loss(pos) 0.1290 | loss(seq) 0.0204 | grad 3.7895 | lr 0.0010 | time_forward 3.6670 | time_backward 4.6780 |
[2023-09-02 02:05:26,788::train::INFO] [train] Iter 04789 | loss 2.8169 | loss(rot) 2.2935 | loss(pos) 0.1778 | loss(seq) 0.3456 | grad 4.0636 | lr 0.0010 | time_forward 3.5630 | time_backward 5.0220 |
[2023-09-02 02:05:36,415::train::INFO] [train] Iter 04790 | loss 2.7938 | loss(rot) 2.4810 | loss(pos) 0.0812 | loss(seq) 0.2316 | grad 3.2860 | lr 0.0010 | time_forward 3.8600 | time_backward 5.7630 |
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