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[2023-10-24 23:06:00,732::train::INFO] [train] Iter 588764 | loss 0.7670 | loss(rot) 0.2436 | loss(pos) 0.2376 | loss(seq) 0.2858 | grad 5.8024 | lr 0.0000 | time_forward 1.3380 | time_backward 1.6210 |
[2023-10-24 23:06:12,008::train::INFO] [train] Iter 588765 | loss 0.9405 | loss(rot) 0.6859 | loss(pos) 0.0501 | loss(seq) 0.2044 | grad 5.6349 | lr 0.0000 | time_forward 5.1290 | time_backward 6.1440 |
[2023-10-24 23:06:20,530::train::INFO] [train] Iter 588766 | loss 0.8055 | loss(rot) 0.2983 | loss(pos) 0.0882 | loss(seq) 0.4190 | grad 3.6655 | lr 0.0000 | time_forward 3.6420 | time_backward 4.8770 |
[2023-10-24 23:06:30,043::train::INFO] [train] Iter 588767 | loss 0.5897 | loss(rot) 0.2838 | loss(pos) 0.0233 | loss(seq) 0.2827 | grad 3.8248 | lr 0.0000 | time_forward 4.1360 | time_backward 5.3740 |
[2023-10-24 23:06:39,247::train::INFO] [train] Iter 588768 | loss 0.1421 | loss(rot) 0.1185 | loss(pos) 0.0228 | loss(seq) 0.0009 | grad 2.1599 | lr 0.0000 | time_forward 3.8890 | time_backward 5.3120 |
[2023-10-24 23:06:42,589::train::INFO] [train] Iter 588769 | loss 0.5104 | loss(rot) 0.0965 | loss(pos) 0.4039 | loss(seq) 0.0099 | grad 6.0631 | lr 0.0000 | time_forward 1.5350 | time_backward 1.8030 |
[2023-10-24 23:06:52,883::train::INFO] [train] Iter 588770 | loss 0.9680 | loss(rot) 0.2882 | loss(pos) 0.4690 | loss(seq) 0.2109 | grad 4.2505 | lr 0.0000 | time_forward 4.2300 | time_backward 6.0490 |
[2023-10-24 23:07:02,045::train::INFO] [train] Iter 588771 | loss 0.3847 | loss(rot) 0.1867 | loss(pos) 0.0327 | loss(seq) 0.1652 | grad 3.7578 | lr 0.0000 | time_forward 3.8370 | time_backward 5.3220 |
[2023-10-24 23:07:12,880::train::INFO] [train] Iter 588772 | loss 0.7956 | loss(rot) 0.7794 | loss(pos) 0.0135 | loss(seq) 0.0027 | grad 4.2167 | lr 0.0000 | time_forward 4.5490 | time_backward 6.2830 |
[2023-10-24 23:07:15,706::train::INFO] [train] Iter 588773 | loss 0.1621 | loss(rot) 0.1131 | loss(pos) 0.0441 | loss(seq) 0.0050 | grad 1.8294 | lr 0.0000 | time_forward 1.3110 | time_backward 1.5120 |
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