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[2023-10-23 23:24:52,181::train::INFO] [train] Iter 576575 | loss 1.5567 | loss(rot) 1.5125 | loss(pos) 0.0436 | loss(seq) 0.0006 | grad 3.6046 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4590 |
[2023-10-23 23:25:02,102::train::INFO] [train] Iter 576576 | loss 0.4966 | loss(rot) 0.1295 | loss(pos) 0.1560 | loss(seq) 0.2111 | grad 3.4709 | lr 0.0000 | time_forward 4.0400 | time_backward 5.8770 |
[2023-10-23 23:25:08,493::train::INFO] [train] Iter 576577 | loss 1.4743 | loss(rot) 0.6987 | loss(pos) 0.1813 | loss(seq) 0.5943 | grad 3.2354 | lr 0.0000 | time_forward 2.6520 | time_backward 3.7360 |
[2023-10-23 23:25:16,416::train::INFO] [train] Iter 576578 | loss 1.5667 | loss(rot) 0.9716 | loss(pos) 0.2748 | loss(seq) 0.3203 | grad 7.7914 | lr 0.0000 | time_forward 3.3250 | time_backward 4.5860 |
[2023-10-23 23:25:24,354::train::INFO] [train] Iter 576579 | loss 0.4396 | loss(rot) 0.4174 | loss(pos) 0.0158 | loss(seq) 0.0063 | grad 4.4459 | lr 0.0000 | time_forward 3.3240 | time_backward 4.6120 |
[2023-10-23 23:25:32,661::train::INFO] [train] Iter 576580 | loss 0.3497 | loss(rot) 0.1183 | loss(pos) 0.0318 | loss(seq) 0.1996 | grad 2.4947 | lr 0.0000 | time_forward 3.5100 | time_backward 4.7930 |
[2023-10-23 23:25:40,903::train::INFO] [train] Iter 576581 | loss 1.3166 | loss(rot) 1.2879 | loss(pos) 0.0174 | loss(seq) 0.0113 | grad 3.7540 | lr 0.0000 | time_forward 3.4900 | time_backward 4.7490 |
[2023-10-23 23:25:43,722::train::INFO] [train] Iter 576582 | loss 0.0777 | loss(rot) 0.0438 | loss(pos) 0.0208 | loss(seq) 0.0130 | grad 1.7785 | lr 0.0000 | time_forward 1.3660 | time_backward 1.4490 |
[2023-10-23 23:25:52,026::train::INFO] [train] Iter 576583 | loss 1.2331 | loss(rot) 1.0710 | loss(pos) 0.0248 | loss(seq) 0.1374 | grad 6.9609 | lr 0.0000 | time_forward 3.5140 | time_backward 4.7640 |
[2023-10-23 23:25:54,777::train::INFO] [train] Iter 576584 | loss 0.4479 | loss(rot) 0.1358 | loss(pos) 0.0208 | loss(seq) 0.2913 | grad 3.1249 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4240 |
[2023-10-23 23:26:00,680::train::INFO] [train] Iter 576585 | loss 0.4366 | loss(rot) 0.4110 | loss(pos) 0.0255 | loss(seq) 0.0001 | grad 3.3566 | lr 0.0000 | time_forward 2.5470 | time_backward 3.3300 |
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