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[2023-09-01 23:37:29,753::train::INFO] [train] Iter 03582 | loss 2.1058 | loss(rot) 1.7272 | loss(pos) 0.2340 | loss(seq) 0.1445 | grad 5.9543 | lr 0.0010 | time_forward 1.2400 | time_backward 1.4910 |
[2023-09-01 23:37:39,811::train::INFO] [train] Iter 03583 | loss 1.7048 | loss(rot) 0.7835 | loss(pos) 0.5773 | loss(seq) 0.3440 | grad 4.7188 | lr 0.0010 | time_forward 4.2590 | time_backward 5.7950 |
[2023-09-01 23:37:42,499::train::INFO] [train] Iter 03584 | loss 3.4183 | loss(rot) 2.9885 | loss(pos) 0.2547 | loss(seq) 0.1752 | grad 5.6702 | lr 0.0010 | time_forward 1.2870 | time_backward 1.3870 |
[2023-09-01 23:37:49,045::train::INFO] [train] Iter 03585 | loss 1.3350 | loss(rot) 0.0397 | loss(pos) 1.2891 | loss(seq) 0.0062 | grad 4.3925 | lr 0.0010 | time_forward 2.6780 | time_backward 3.8640 |
[2023-09-01 23:37:57,498::train::INFO] [train] Iter 03586 | loss 1.5232 | loss(rot) 0.4019 | loss(pos) 0.8808 | loss(seq) 0.2405 | grad 5.3475 | lr 0.0010 | time_forward 3.5270 | time_backward 4.9220 |
[2023-09-01 23:38:07,482::train::INFO] [train] Iter 03587 | loss 1.0464 | loss(rot) 0.4803 | loss(pos) 0.3750 | loss(seq) 0.1911 | grad 2.6426 | lr 0.0010 | time_forward 4.1910 | time_backward 5.7890 |
[2023-09-01 23:38:09,726::train::INFO] [train] Iter 03588 | loss 1.3830 | loss(rot) 0.6523 | loss(pos) 0.2931 | loss(seq) 0.4376 | grad 2.7580 | lr 0.0010 | time_forward 1.0360 | time_backward 1.2030 |
[2023-09-01 23:38:17,790::train::INFO] [train] Iter 03589 | loss 2.8380 | loss(rot) 1.4479 | loss(pos) 0.9264 | loss(seq) 0.4637 | grad 5.8929 | lr 0.0010 | time_forward 3.3380 | time_backward 4.7220 |
[2023-09-01 23:38:27,672::train::INFO] [train] Iter 03590 | loss 3.7478 | loss(rot) 3.2395 | loss(pos) 0.5081 | loss(seq) 0.0001 | grad 6.9032 | lr 0.0010 | time_forward 4.1110 | time_backward 5.7670 |
[2023-09-01 23:38:37,679::train::INFO] [train] Iter 03591 | loss 1.8712 | loss(rot) 1.4283 | loss(pos) 0.1764 | loss(seq) 0.2664 | grad 3.8936 | lr 0.0010 | time_forward 4.2360 | time_backward 5.7670 |
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