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[2023-10-25 08:12:36,692::train::INFO] [train] Iter 593659 | loss 1.0722 | loss(rot) 0.6716 | loss(pos) 0.0423 | loss(seq) 0.3582 | grad 3.1511 | lr 0.0000 | time_forward 3.2170 | time_backward 4.4620 |
[2023-10-25 08:12:45,034::train::INFO] [train] Iter 593660 | loss 0.1192 | loss(rot) 0.0609 | loss(pos) 0.0272 | loss(seq) 0.0312 | grad 1.5033 | lr 0.0000 | time_forward 3.5690 | time_backward 4.7700 |
[2023-10-25 08:12:51,858::train::INFO] [train] Iter 593661 | loss 0.6667 | loss(rot) 0.5463 | loss(pos) 0.0192 | loss(seq) 0.1012 | grad 37.8817 | lr 0.0000 | time_forward 2.9520 | time_backward 3.8700 |
[2023-10-25 08:12:59,364::train::INFO] [train] Iter 593662 | loss 0.2606 | loss(rot) 0.0760 | loss(pos) 0.0184 | loss(seq) 0.1662 | grad 1.8839 | lr 0.0000 | time_forward 3.2270 | time_backward 4.2760 |
[2023-10-25 08:13:06,652::train::INFO] [train] Iter 593663 | loss 1.7574 | loss(rot) 1.4259 | loss(pos) 0.0685 | loss(seq) 0.2630 | grad 5.7281 | lr 0.0000 | time_forward 3.0920 | time_backward 4.1940 |
[2023-10-25 08:13:09,422::train::INFO] [train] Iter 593664 | loss 0.2441 | loss(rot) 0.1253 | loss(pos) 0.0384 | loss(seq) 0.0803 | grad 2.4885 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4520 |
[2023-10-25 08:13:16,624::train::INFO] [train] Iter 593665 | loss 0.5129 | loss(rot) 0.2620 | loss(pos) 0.0452 | loss(seq) 0.2057 | grad 4.3035 | lr 0.0000 | time_forward 3.0120 | time_backward 4.1870 |
[2023-10-25 08:13:25,013::train::INFO] [train] Iter 593666 | loss 0.7114 | loss(rot) 0.6821 | loss(pos) 0.0272 | loss(seq) 0.0021 | grad 3.0238 | lr 0.0000 | time_forward 3.5870 | time_backward 4.7990 |
[2023-10-25 08:13:27,811::train::INFO] [train] Iter 593667 | loss 0.4791 | loss(rot) 0.1871 | loss(pos) 0.1789 | loss(seq) 0.1131 | grad 4.7824 | lr 0.0000 | time_forward 1.3480 | time_backward 1.4460 |
[2023-10-25 08:13:36,082::train::INFO] [train] Iter 593668 | loss 0.5208 | loss(rot) 0.2368 | loss(pos) 0.0698 | loss(seq) 0.2141 | grad 4.1235 | lr 0.0000 | time_forward 3.5280 | time_backward 4.7410 |
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