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[2023-10-23 01:03:26,757::train::INFO] [train] Iter 564189 | loss 0.9538 | loss(rot) 0.9086 | loss(pos) 0.0362 | loss(seq) 0.0090 | grad 13.3592 | lr 0.0000 | time_forward 2.9660 | time_backward 3.7960 |
[2023-10-23 01:03:34,048::train::INFO] [train] Iter 564190 | loss 0.1976 | loss(rot) 0.1488 | loss(pos) 0.0486 | loss(seq) 0.0002 | grad 2.1028 | lr 0.0000 | time_forward 3.1430 | time_backward 4.1440 |
[2023-10-23 01:03:40,661::train::INFO] [train] Iter 564191 | loss 0.3872 | loss(rot) 0.1782 | loss(pos) 0.0204 | loss(seq) 0.1886 | grad 2.4227 | lr 0.0000 | time_forward 2.8690 | time_backward 3.7400 |
[2023-10-23 01:03:48,098::train::INFO] [train] Iter 564192 | loss 0.2114 | loss(rot) 0.1791 | loss(pos) 0.0208 | loss(seq) 0.0115 | grad 3.3197 | lr 0.0000 | time_forward 3.2080 | time_backward 4.2250 |
[2023-10-23 01:03:55,535::train::INFO] [train] Iter 564193 | loss 2.4377 | loss(rot) 2.3873 | loss(pos) 0.0350 | loss(seq) 0.0153 | grad 4.4712 | lr 0.0000 | time_forward 3.2390 | time_backward 4.1940 |
[2023-10-23 01:03:58,314::train::INFO] [train] Iter 564194 | loss 1.6904 | loss(rot) 1.4522 | loss(pos) 0.0313 | loss(seq) 0.2069 | grad 6.0562 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4490 |
[2023-10-23 01:04:06,459::train::INFO] [train] Iter 564195 | loss 0.2379 | loss(rot) 0.0953 | loss(pos) 0.0299 | loss(seq) 0.1127 | grad 2.0102 | lr 0.0000 | time_forward 3.3600 | time_backward 4.7830 |
[2023-10-23 01:04:11,393::train::INFO] [train] Iter 564196 | loss 0.4097 | loss(rot) 0.1688 | loss(pos) 0.0375 | loss(seq) 0.2034 | grad 4.6245 | lr 0.0000 | time_forward 2.1790 | time_backward 2.7510 |
[2023-10-23 01:04:19,312::train::INFO] [train] Iter 564197 | loss 1.4036 | loss(rot) 0.8845 | loss(pos) 0.2478 | loss(seq) 0.2714 | grad 4.1483 | lr 0.0000 | time_forward 3.4640 | time_backward 4.4520 |
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