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[2023-10-23 07:20:46,705::train::INFO] [train] Iter 567985 | loss 1.0150 | loss(rot) 0.9871 | loss(pos) 0.0173 | loss(seq) 0.0105 | grad 42.7459 | lr 0.0000 | time_forward 1.2450 | time_backward 1.4810 |
[2023-10-23 07:20:49,444::train::INFO] [train] Iter 567986 | loss 1.4272 | loss(rot) 0.8926 | loss(pos) 0.1834 | loss(seq) 0.3512 | grad 8.8699 | lr 0.0000 | time_forward 1.2540 | time_backward 1.4810 |
[2023-10-23 07:20:51,687::train::INFO] [train] Iter 567987 | loss 0.6824 | loss(rot) 0.4255 | loss(pos) 0.0462 | loss(seq) 0.2106 | grad 2.9007 | lr 0.0000 | time_forward 1.0480 | time_backward 1.1920 |
[2023-10-23 07:20:59,901::train::INFO] [train] Iter 567988 | loss 0.1671 | loss(rot) 0.0983 | loss(pos) 0.0239 | loss(seq) 0.0449 | grad 2.3045 | lr 0.0000 | time_forward 3.3970 | time_backward 4.8060 |
[2023-10-23 07:21:06,646::train::INFO] [train] Iter 567989 | loss 0.5845 | loss(rot) 0.0347 | loss(pos) 0.5472 | loss(seq) 0.0027 | grad 5.5749 | lr 0.0000 | time_forward 2.9030 | time_backward 3.8390 |
[2023-10-23 07:21:13,680::train::INFO] [train] Iter 567990 | loss 1.1825 | loss(rot) 0.3082 | loss(pos) 0.5391 | loss(seq) 0.3352 | grad 4.3322 | lr 0.0000 | time_forward 3.0180 | time_backward 4.0120 |
[2023-10-23 07:21:20,669::train::INFO] [train] Iter 567991 | loss 0.3021 | loss(rot) 0.0697 | loss(pos) 0.2237 | loss(seq) 0.0087 | grad 4.5893 | lr 0.0000 | time_forward 3.0110 | time_backward 3.9750 |
[2023-10-23 07:21:22,846::train::INFO] [train] Iter 567992 | loss 0.2244 | loss(rot) 0.1126 | loss(pos) 0.0108 | loss(seq) 0.1010 | grad 1.8790 | lr 0.0000 | time_forward 1.0110 | time_backward 1.1630 |
[2023-10-23 07:21:25,475::train::INFO] [train] Iter 567993 | loss 0.7216 | loss(rot) 0.0245 | loss(pos) 0.6941 | loss(seq) 0.0030 | grad 8.4065 | lr 0.0000 | time_forward 1.2380 | time_backward 1.3880 |
[2023-10-23 07:21:28,584::train::INFO] [train] Iter 567994 | loss 0.7959 | loss(rot) 0.2748 | loss(pos) 0.1815 | loss(seq) 0.3396 | grad 5.3447 | lr 0.0000 | time_forward 1.4360 | time_backward 1.6700 |
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