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[2023-09-02 03:55:04,385::train::INFO] [train] Iter 05680 | loss 3.1416 | loss(rot) 2.6035 | loss(pos) 0.1669 | loss(seq) 0.3712 | grad 5.9783 | lr 0.0010 | time_forward 6.0610 | time_backward 6.5110 |
[2023-09-02 03:55:06,662::train::INFO] [train] Iter 05681 | loss 2.7815 | loss(rot) 0.0785 | loss(pos) 2.7014 | loss(seq) 0.0017 | grad 5.3765 | lr 0.0010 | time_forward 1.0550 | time_backward 1.2190 |
[2023-09-02 03:55:16,929::train::INFO] [train] Iter 05682 | loss 2.2322 | loss(rot) 0.0604 | loss(pos) 2.1693 | loss(seq) 0.0025 | grad 5.9599 | lr 0.0010 | time_forward 4.1920 | time_backward 6.0710 |
[2023-09-02 03:55:19,492::train::INFO] [train] Iter 05683 | loss 2.9274 | loss(rot) 2.1535 | loss(pos) 0.3032 | loss(seq) 0.4708 | grad 4.0578 | lr 0.0010 | time_forward 1.1860 | time_backward 1.3730 |
[2023-09-02 03:55:22,382::train::INFO] [train] Iter 05684 | loss 2.4412 | loss(rot) 1.8505 | loss(pos) 0.1117 | loss(seq) 0.4790 | grad 3.1646 | lr 0.0010 | time_forward 1.4130 | time_backward 1.4460 |
[2023-09-02 03:55:25,261::train::INFO] [train] Iter 05685 | loss 1.5246 | loss(rot) 0.0844 | loss(pos) 1.4260 | loss(seq) 0.0141 | grad 5.2849 | lr 0.0010 | time_forward 1.4290 | time_backward 1.4460 |
[2023-09-02 03:55:35,116::train::INFO] [train] Iter 05686 | loss 2.0575 | loss(rot) 1.2593 | loss(pos) 0.2663 | loss(seq) 0.5320 | grad 3.1470 | lr 0.0010 | time_forward 4.0260 | time_backward 5.8250 |
[2023-09-02 03:55:37,889::train::INFO] [train] Iter 05687 | loss 2.6573 | loss(rot) 2.5252 | loss(pos) 0.0806 | loss(seq) 0.0515 | grad 5.4860 | lr 0.0010 | time_forward 1.2860 | time_backward 1.4540 |
[2023-09-02 03:55:40,274::train::INFO] [train] Iter 05688 | loss 2.4401 | loss(rot) 1.9937 | loss(pos) 0.4134 | loss(seq) 0.0331 | grad 4.1825 | lr 0.0010 | time_forward 1.1440 | time_backward 1.2370 |
[2023-09-02 03:55:42,983::train::INFO] [train] Iter 05689 | loss 2.5526 | loss(rot) 2.3913 | loss(pos) 0.1349 | loss(seq) 0.0264 | grad 4.5597 | lr 0.0010 | time_forward 1.3080 | time_backward 1.3980 |
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