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[2023-10-25 03:31:29,878::train::INFO] [train] Iter 591061 | loss 0.8682 | loss(rot) 0.1959 | loss(pos) 0.4152 | loss(seq) 0.2570 | grad 2.8403 | lr 0.0000 | time_forward 3.3890 | time_backward 4.5860 |
[2023-10-25 03:31:32,576::train::INFO] [train] Iter 591062 | loss 1.3898 | loss(rot) 1.2958 | loss(pos) 0.0265 | loss(seq) 0.0675 | grad 4.1945 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4170 |
[2023-10-25 03:31:39,692::train::INFO] [train] Iter 591063 | loss 0.3371 | loss(rot) 0.0585 | loss(pos) 0.2637 | loss(seq) 0.0149 | grad 5.8724 | lr 0.0000 | time_forward 2.9810 | time_backward 4.1040 |
[2023-10-25 03:31:46,397::train::INFO] [train] Iter 591064 | loss 0.2815 | loss(rot) 0.1000 | loss(pos) 0.0214 | loss(seq) 0.1601 | grad 1.6605 | lr 0.0000 | time_forward 2.8450 | time_backward 3.8560 |
[2023-10-25 03:31:55,280::train::INFO] [train] Iter 591065 | loss 0.7282 | loss(rot) 0.1885 | loss(pos) 0.0685 | loss(seq) 0.4712 | grad 3.2179 | lr 0.0000 | time_forward 3.6840 | time_backward 5.1960 |
[2023-10-25 03:32:03,093::train::INFO] [train] Iter 591066 | loss 0.2054 | loss(rot) 0.0760 | loss(pos) 0.1236 | loss(seq) 0.0058 | grad 4.1292 | lr 0.0000 | time_forward 3.3130 | time_backward 4.4970 |
[2023-10-25 03:32:10,836::train::INFO] [train] Iter 591067 | loss 0.5009 | loss(rot) 0.3356 | loss(pos) 0.0265 | loss(seq) 0.1388 | grad 22.0497 | lr 0.0000 | time_forward 3.3080 | time_backward 4.4320 |
[2023-10-25 03:32:18,227::train::INFO] [train] Iter 591068 | loss 1.9738 | loss(rot) 1.5930 | loss(pos) 0.0686 | loss(seq) 0.3122 | grad 9.9233 | lr 0.0000 | time_forward 3.1800 | time_backward 4.2080 |
[2023-10-25 03:32:21,403::train::INFO] [train] Iter 591069 | loss 0.4822 | loss(rot) 0.1635 | loss(pos) 0.2852 | loss(seq) 0.0336 | grad 2.8837 | lr 0.0000 | time_forward 1.4260 | time_backward 1.7470 |
[2023-10-25 03:32:28,599::train::INFO] [train] Iter 591070 | loss 0.3399 | loss(rot) 0.1208 | loss(pos) 0.1893 | loss(seq) 0.0299 | grad 5.2772 | lr 0.0000 | time_forward 3.0760 | time_backward 4.1180 |
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