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[2023-10-23 03:42:46,408::train::INFO] [train] Iter 565787 | loss 0.3111 | loss(rot) 0.0483 | loss(pos) 0.0603 | loss(seq) 0.2025 | grad 2.4771 | lr 0.0000 | time_forward 3.0800 | time_backward 3.9990 |
[2023-10-23 03:42:49,623::train::INFO] [train] Iter 565788 | loss 0.8007 | loss(rot) 0.6563 | loss(pos) 0.0372 | loss(seq) 0.1072 | grad 3.9274 | lr 0.0000 | time_forward 1.4320 | time_backward 1.7790 |
[2023-10-23 03:42:52,292::train::INFO] [train] Iter 565789 | loss 0.3014 | loss(rot) 0.0392 | loss(pos) 0.2268 | loss(seq) 0.0354 | grad 4.9941 | lr 0.0000 | time_forward 1.2610 | time_backward 1.4040 |
[2023-10-23 03:42:59,300::train::INFO] [train] Iter 565790 | loss 0.2388 | loss(rot) 0.1088 | loss(pos) 0.0954 | loss(seq) 0.0347 | grad 2.2008 | lr 0.0000 | time_forward 3.0610 | time_backward 3.9440 |
[2023-10-23 03:43:02,247::train::INFO] [train] Iter 565791 | loss 0.4962 | loss(rot) 0.2209 | loss(pos) 0.2628 | loss(seq) 0.0124 | grad 5.3888 | lr 0.0000 | time_forward 1.4500 | time_backward 1.4940 |
[2023-10-23 03:43:05,100::train::INFO] [train] Iter 565792 | loss 1.8344 | loss(rot) 1.2560 | loss(pos) 0.0996 | loss(seq) 0.4789 | grad 6.1859 | lr 0.0000 | time_forward 1.3970 | time_backward 1.4520 |
[2023-10-23 03:43:13,009::train::INFO] [train] Iter 565793 | loss 0.3890 | loss(rot) 0.1512 | loss(pos) 0.0097 | loss(seq) 0.2282 | grad 2.6820 | lr 0.0000 | time_forward 3.6920 | time_backward 4.2140 |
[2023-10-23 03:43:15,479::train::INFO] [train] Iter 565794 | loss 0.3972 | loss(rot) 0.0198 | loss(pos) 0.3730 | loss(seq) 0.0045 | grad 7.8220 | lr 0.0000 | time_forward 1.2680 | time_backward 1.1960 |
[2023-10-23 03:43:24,286::train::INFO] [train] Iter 565795 | loss 1.2824 | loss(rot) 0.7173 | loss(pos) 0.0988 | loss(seq) 0.4663 | grad 5.6992 | lr 0.0000 | time_forward 4.0400 | time_backward 4.7520 |
[2023-10-23 03:43:27,249::train::INFO] [train] Iter 565796 | loss 1.4493 | loss(rot) 1.2976 | loss(pos) 0.0737 | loss(seq) 0.0780 | grad 4.1777 | lr 0.0000 | time_forward 1.5310 | time_backward 1.4290 |
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