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[2023-09-03 04:40:10,531::train::INFO] [train] Iter 18068 | loss 2.8754 | loss(rot) 2.2722 | loss(pos) 0.2278 | loss(seq) 0.3754 | grad 3.9939 | lr 0.0010 | time_forward 1.1860 | time_backward 1.3950 |
[2023-09-03 04:40:13,205::train::INFO] [train] Iter 18069 | loss 0.6862 | loss(rot) 0.4130 | loss(pos) 0.1480 | loss(seq) 0.1251 | grad 4.9564 | lr 0.0010 | time_forward 1.2210 | time_backward 1.4290 |
[2023-09-03 04:40:20,496::train::INFO] [train] Iter 18070 | loss 1.3417 | loss(rot) 1.1615 | loss(pos) 0.1799 | loss(seq) 0.0003 | grad 3.7215 | lr 0.0010 | time_forward 2.8590 | time_backward 4.4290 |
[2023-09-03 04:40:27,182::train::INFO] [train] Iter 18071 | loss 0.9895 | loss(rot) 0.4436 | loss(pos) 0.2100 | loss(seq) 0.3359 | grad 5.7908 | lr 0.0010 | time_forward 2.7990 | time_backward 3.8830 |
[2023-09-03 04:40:34,095::train::INFO] [train] Iter 18072 | loss 1.1709 | loss(rot) 0.9122 | loss(pos) 0.0968 | loss(seq) 0.1618 | grad 8.8699 | lr 0.0010 | time_forward 2.9380 | time_backward 3.9720 |
[2023-09-03 04:40:36,313::train::INFO] [train] Iter 18073 | loss 2.3690 | loss(rot) 1.7375 | loss(pos) 0.2324 | loss(seq) 0.3991 | grad 6.2634 | lr 0.0010 | time_forward 1.0260 | time_backward 1.1900 |
[2023-09-03 04:40:43,383::train::INFO] [train] Iter 18074 | loss 1.3977 | loss(rot) 0.0870 | loss(pos) 1.2984 | loss(seq) 0.0124 | grad 8.2686 | lr 0.0010 | time_forward 2.8900 | time_backward 4.1760 |
[2023-09-03 04:40:51,592::train::INFO] [train] Iter 18075 | loss 0.9492 | loss(rot) 0.2623 | loss(pos) 0.2061 | loss(seq) 0.4809 | grad 4.0858 | lr 0.0010 | time_forward 3.4730 | time_backward 4.7340 |
[2023-09-03 04:40:59,817::train::INFO] [train] Iter 18076 | loss 0.9343 | loss(rot) 0.8696 | loss(pos) 0.0629 | loss(seq) 0.0018 | grad 4.3260 | lr 0.0010 | time_forward 3.2390 | time_backward 4.9820 |
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