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[2023-10-25 01:40:11,323::train::INFO] [train] Iter 590061 | loss 2.0953 | loss(rot) 1.6351 | loss(pos) 0.1493 | loss(seq) 0.3109 | grad 4.1750 | lr 0.0000 | time_forward 3.7980 | time_backward 5.2130 |
[2023-10-25 01:40:13,999::train::INFO] [train] Iter 590062 | loss 0.7216 | loss(rot) 0.6376 | loss(pos) 0.0423 | loss(seq) 0.0417 | grad 3.5432 | lr 0.0000 | time_forward 1.2930 | time_backward 1.3800 |
[2023-10-25 01:40:21,602::train::INFO] [train] Iter 590063 | loss 0.3590 | loss(rot) 0.1222 | loss(pos) 0.0325 | loss(seq) 0.2043 | grad 2.3084 | lr 0.0000 | time_forward 3.2410 | time_backward 4.3320 |
[2023-10-25 01:40:30,544::train::INFO] [train] Iter 590064 | loss 1.2105 | loss(rot) 1.1853 | loss(pos) 0.0192 | loss(seq) 0.0060 | grad 2.6317 | lr 0.0000 | time_forward 3.6950 | time_backward 5.2430 |
[2023-10-25 01:40:37,752::train::INFO] [train] Iter 590065 | loss 1.1226 | loss(rot) 0.7080 | loss(pos) 0.0520 | loss(seq) 0.3626 | grad 3.2424 | lr 0.0000 | time_forward 3.0570 | time_backward 4.1490 |
[2023-10-25 01:40:46,794::train::INFO] [train] Iter 590066 | loss 0.2003 | loss(rot) 0.0830 | loss(pos) 0.0304 | loss(seq) 0.0869 | grad 1.8665 | lr 0.0000 | time_forward 3.7130 | time_backward 5.3250 |
[2023-10-25 01:40:55,711::train::INFO] [train] Iter 590067 | loss 0.8666 | loss(rot) 0.0173 | loss(pos) 0.6965 | loss(seq) 0.1528 | grad 8.8783 | lr 0.0000 | time_forward 3.8380 | time_backward 5.0770 |
[2023-10-25 01:40:58,888::train::INFO] [train] Iter 590068 | loss 0.5057 | loss(rot) 0.0469 | loss(pos) 0.2963 | loss(seq) 0.1625 | grad 2.7914 | lr 0.0000 | time_forward 1.4470 | time_backward 1.7260 |
[2023-10-25 01:41:01,621::train::INFO] [train] Iter 590069 | loss 0.1777 | loss(rot) 0.0432 | loss(pos) 0.0422 | loss(seq) 0.0924 | grad 2.9031 | lr 0.0000 | time_forward 1.2830 | time_backward 1.4360 |
[2023-10-25 01:41:09,501::train::INFO] [train] Iter 590070 | loss 0.3584 | loss(rot) 0.3135 | loss(pos) 0.0177 | loss(seq) 0.0273 | grad 3.8977 | lr 0.0000 | time_forward 3.3650 | time_backward 4.5120 |
[2023-10-25 01:41:16,846::train::INFO] [train] Iter 590071 | loss 0.5560 | loss(rot) 0.1412 | loss(pos) 0.0987 | loss(seq) 0.3161 | grad 3.7066 | lr 0.0000 | time_forward 3.1280 | time_backward 4.2140 |
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