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[2023-10-22 19:50:11,779::train::INFO] [train] Iter 561291 | loss 0.6751 | loss(rot) 0.0443 | loss(pos) 0.6274 | loss(seq) 0.0033 | grad 10.0653 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4700 |
[2023-10-22 19:50:15,131::train::INFO] [train] Iter 561292 | loss 1.1964 | loss(rot) 1.1360 | loss(pos) 0.0587 | loss(seq) 0.0017 | grad 4.0025 | lr 0.0000 | time_forward 1.5100 | time_backward 1.8380 |
[2023-10-22 19:50:20,960::train::INFO] [train] Iter 561293 | loss 1.5927 | loss(rot) 1.0419 | loss(pos) 0.1234 | loss(seq) 0.4274 | grad 2.5130 | lr 0.0000 | time_forward 2.4910 | time_backward 3.3350 |
[2023-10-22 19:50:28,592::train::INFO] [train] Iter 561294 | loss 0.5157 | loss(rot) 0.4869 | loss(pos) 0.0280 | loss(seq) 0.0008 | grad 2.2310 | lr 0.0000 | time_forward 3.1920 | time_backward 4.4380 |
[2023-10-22 19:50:38,087::train::INFO] [train] Iter 561295 | loss 0.2859 | loss(rot) 0.1065 | loss(pos) 0.0800 | loss(seq) 0.0994 | grad 2.4560 | lr 0.0000 | time_forward 3.9470 | time_backward 5.5440 |
[2023-10-22 19:50:40,681::train::INFO] [train] Iter 561296 | loss 0.8217 | loss(rot) 0.0067 | loss(pos) 0.8146 | loss(seq) 0.0004 | grad 17.1542 | lr 0.0000 | time_forward 1.2470 | time_backward 1.3440 |
[2023-10-22 19:50:43,394::train::INFO] [train] Iter 561297 | loss 0.4211 | loss(rot) 0.0944 | loss(pos) 0.2475 | loss(seq) 0.0792 | grad 2.9205 | lr 0.0000 | time_forward 1.2540 | time_backward 1.4440 |
[2023-10-22 19:50:46,142::train::INFO] [train] Iter 561298 | loss 0.6829 | loss(rot) 0.0358 | loss(pos) 0.3784 | loss(seq) 0.2687 | grad 5.2581 | lr 0.0000 | time_forward 1.3170 | time_backward 1.3910 |
[2023-10-22 19:50:55,114::train::INFO] [train] Iter 561299 | loss 0.6159 | loss(rot) 0.4004 | loss(pos) 0.0701 | loss(seq) 0.1454 | grad 3.3255 | lr 0.0000 | time_forward 3.7240 | time_backward 5.2430 |
[2023-10-22 19:51:02,897::train::INFO] [train] Iter 561300 | loss 0.5052 | loss(rot) 0.4496 | loss(pos) 0.0294 | loss(seq) 0.0261 | grad 4.4334 | lr 0.0000 | time_forward 3.3030 | time_backward 4.4770 |
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