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[2023-10-23 00:53:07,145::train::INFO] [train] Iter 564088 | loss 0.3131 | loss(rot) 0.2163 | loss(pos) 0.0197 | loss(seq) 0.0770 | grad 3.1083 | lr 0.0000 | time_forward 2.8930 | time_backward 3.6990 |
[2023-10-23 00:53:14,032::train::INFO] [train] Iter 564089 | loss 0.2581 | loss(rot) 0.2263 | loss(pos) 0.0318 | loss(seq) 0.0000 | grad 2.3313 | lr 0.0000 | time_forward 2.9180 | time_backward 3.9660 |
[2023-10-23 00:53:20,578::train::INFO] [train] Iter 564090 | loss 1.0255 | loss(rot) 0.9285 | loss(pos) 0.0560 | loss(seq) 0.0410 | grad 6.0404 | lr 0.0000 | time_forward 2.8580 | time_backward 3.6850 |
[2023-10-23 00:53:27,104::train::INFO] [train] Iter 564091 | loss 0.5163 | loss(rot) 0.2481 | loss(pos) 0.0662 | loss(seq) 0.2020 | grad 3.8549 | lr 0.0000 | time_forward 2.8340 | time_backward 3.6880 |
[2023-10-23 00:53:33,437::train::INFO] [train] Iter 564092 | loss 0.3688 | loss(rot) 0.3532 | loss(pos) 0.0099 | loss(seq) 0.0057 | grad 4.3980 | lr 0.0000 | time_forward 2.7340 | time_backward 3.5970 |
[2023-10-23 00:53:40,247::train::INFO] [train] Iter 564093 | loss 0.7220 | loss(rot) 0.5137 | loss(pos) 0.1062 | loss(seq) 0.1020 | grad 2.9216 | lr 0.0000 | time_forward 2.9810 | time_backward 3.8250 |
[2023-10-23 00:53:42,917::train::INFO] [train] Iter 564094 | loss 0.2971 | loss(rot) 0.0901 | loss(pos) 0.1142 | loss(seq) 0.0927 | grad 5.0610 | lr 0.0000 | time_forward 1.2700 | time_backward 1.3960 |
[2023-10-23 00:53:51,030::train::INFO] [train] Iter 564095 | loss 0.3687 | loss(rot) 0.0998 | loss(pos) 0.2015 | loss(seq) 0.0674 | grad 6.4191 | lr 0.0000 | time_forward 3.5070 | time_backward 4.6030 |
[2023-10-23 00:53:53,694::train::INFO] [train] Iter 564096 | loss 0.9262 | loss(rot) 0.7506 | loss(pos) 0.0945 | loss(seq) 0.0811 | grad 3.9830 | lr 0.0000 | time_forward 1.2860 | time_backward 1.3750 |
[2023-10-23 00:54:00,060::train::INFO] [train] Iter 564097 | loss 0.3157 | loss(rot) 0.1163 | loss(pos) 0.1349 | loss(seq) 0.0645 | grad 3.2062 | lr 0.0000 | time_forward 2.7460 | time_backward 3.6170 |
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