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[2023-09-03 03:11:10,147::train::INFO] [train] Iter 17269 | loss 1.2244 | loss(rot) 0.3986 | loss(pos) 0.2418 | loss(seq) 0.5840 | grad 4.9872 | lr 0.0010 | time_forward 1.2680 | time_backward 1.4890 |
[2023-09-03 03:11:18,475::train::INFO] [train] Iter 17270 | loss 1.0985 | loss(rot) 0.9744 | loss(pos) 0.0854 | loss(seq) 0.0387 | grad 14.8485 | lr 0.0010 | time_forward 3.5210 | time_backward 4.8030 |
[2023-09-03 03:11:21,329::train::INFO] [train] Iter 17271 | loss 1.3298 | loss(rot) 0.6258 | loss(pos) 0.3315 | loss(seq) 0.3725 | grad 9.1394 | lr 0.0010 | time_forward 1.3870 | time_backward 1.4640 |
[2023-09-03 03:11:32,844::train::INFO] [train] Iter 17272 | loss 1.9619 | loss(rot) 1.8225 | loss(pos) 0.1328 | loss(seq) 0.0066 | grad 5.3194 | lr 0.0010 | time_forward 6.2420 | time_backward 5.2700 |
[2023-09-03 03:11:35,041::train::INFO] [train] Iter 17273 | loss 1.1006 | loss(rot) 0.8609 | loss(pos) 0.0799 | loss(seq) 0.1597 | grad 4.1643 | lr 0.0010 | time_forward 1.0240 | time_backward 1.1690 |
[2023-09-03 03:11:37,623::train::INFO] [train] Iter 17274 | loss 0.8307 | loss(rot) 0.7621 | loss(pos) 0.0624 | loss(seq) 0.0062 | grad 13.6823 | lr 0.0010 | time_forward 1.2040 | time_backward 1.3750 |
[2023-09-03 03:11:45,029::train::INFO] [train] Iter 17275 | loss 1.6805 | loss(rot) 1.6374 | loss(pos) 0.0430 | loss(seq) 0.0000 | grad 4.8239 | lr 0.0010 | time_forward 3.0910 | time_backward 4.3110 |
[2023-09-03 03:11:52,886::train::INFO] [train] Iter 17276 | loss 1.0959 | loss(rot) 0.3947 | loss(pos) 0.6764 | loss(seq) 0.0249 | grad 4.3488 | lr 0.0010 | time_forward 3.0920 | time_backward 4.7620 |
[2023-09-03 03:11:55,513::train::INFO] [train] Iter 17277 | loss 1.2681 | loss(rot) 1.1355 | loss(pos) 0.1324 | loss(seq) 0.0001 | grad 7.0603 | lr 0.0010 | time_forward 1.1910 | time_backward 1.4320 |
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