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[2023-10-23 14:28:26,209::train::INFO] [train] Iter 571881 | loss 0.9238 | loss(rot) 0.8850 | loss(pos) 0.0387 | loss(seq) 0.0000 | grad 131.7960 | lr 0.0000 | time_forward 1.8280 | time_backward 2.3090 |
[2023-10-23 14:28:32,202::train::INFO] [train] Iter 571882 | loss 1.0254 | loss(rot) 0.7687 | loss(pos) 0.0225 | loss(seq) 0.2342 | grad 3.6783 | lr 0.0000 | time_forward 2.5570 | time_backward 3.4100 |
[2023-10-23 14:28:41,021::train::INFO] [train] Iter 571883 | loss 1.1428 | loss(rot) 0.6339 | loss(pos) 0.2337 | loss(seq) 0.2752 | grad 43.2485 | lr 0.0000 | time_forward 3.7920 | time_backward 5.0010 |
[2023-10-23 14:28:44,394::train::INFO] [train] Iter 571884 | loss 1.5626 | loss(rot) 1.5273 | loss(pos) 0.0307 | loss(seq) 0.0046 | grad 4.4061 | lr 0.0000 | time_forward 1.5050 | time_backward 1.8650 |
[2023-10-23 14:28:50,818::train::INFO] [train] Iter 571885 | loss 0.2624 | loss(rot) 0.1917 | loss(pos) 0.0295 | loss(seq) 0.0412 | grad 2.2508 | lr 0.0000 | time_forward 2.7460 | time_backward 3.6740 |
[2023-10-23 14:28:59,638::train::INFO] [train] Iter 571886 | loss 1.5335 | loss(rot) 1.0428 | loss(pos) 0.0829 | loss(seq) 0.4077 | grad 4.2999 | lr 0.0000 | time_forward 3.6280 | time_backward 5.1780 |
[2023-10-23 14:29:08,753::train::INFO] [train] Iter 571887 | loss 1.3919 | loss(rot) 1.1392 | loss(pos) 0.0341 | loss(seq) 0.2186 | grad 5.4091 | lr 0.0000 | time_forward 3.7230 | time_backward 5.3890 |
[2023-10-23 14:29:17,622::train::INFO] [train] Iter 571888 | loss 1.8903 | loss(rot) 1.6756 | loss(pos) 0.0438 | loss(seq) 0.1709 | grad 4.7666 | lr 0.0000 | time_forward 3.6920 | time_backward 5.1740 |
[2023-10-23 14:29:24,507::train::INFO] [train] Iter 571889 | loss 0.2841 | loss(rot) 0.2127 | loss(pos) 0.0324 | loss(seq) 0.0389 | grad 2.7091 | lr 0.0000 | time_forward 2.9600 | time_backward 3.9220 |
[2023-10-23 14:29:27,330::train::INFO] [train] Iter 571890 | loss 1.2883 | loss(rot) 0.7720 | loss(pos) 0.1930 | loss(seq) 0.3234 | grad 10.8307 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4940 |
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