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[2023-10-24 04:55:30,328::train::INFO] [train] Iter 579373 | loss 0.4946 | loss(rot) 0.2161 | loss(pos) 0.0637 | loss(seq) 0.2148 | grad 4.0235 | lr 0.0000 | time_forward 3.3870 | time_backward 4.6970 |
[2023-10-24 04:55:38,798::train::INFO] [train] Iter 579374 | loss 0.4631 | loss(rot) 0.0474 | loss(pos) 0.4096 | loss(seq) 0.0061 | grad 4.9321 | lr 0.0000 | time_forward 3.5260 | time_backward 4.9420 |
[2023-10-24 04:55:47,543::train::INFO] [train] Iter 579375 | loss 0.2589 | loss(rot) 0.0773 | loss(pos) 0.0322 | loss(seq) 0.1495 | grad 1.9566 | lr 0.0000 | time_forward 3.7030 | time_backward 5.0380 |
[2023-10-24 04:55:50,844::train::INFO] [train] Iter 579376 | loss 0.9988 | loss(rot) 0.5842 | loss(pos) 0.1272 | loss(seq) 0.2874 | grad 2.4396 | lr 0.0000 | time_forward 1.4890 | time_backward 1.8080 |
[2023-10-24 04:55:53,567::train::INFO] [train] Iter 579377 | loss 0.4614 | loss(rot) 0.2035 | loss(pos) 0.0733 | loss(seq) 0.1846 | grad 3.4974 | lr 0.0000 | time_forward 1.2650 | time_backward 1.4430 |
[2023-10-24 04:55:56,375::train::INFO] [train] Iter 579378 | loss 0.2574 | loss(rot) 0.0533 | loss(pos) 0.1285 | loss(seq) 0.0756 | grad 2.9100 | lr 0.0000 | time_forward 1.3400 | time_backward 1.4650 |
[2023-10-24 04:55:59,158::train::INFO] [train] Iter 579379 | loss 1.3955 | loss(rot) 1.0660 | loss(pos) 0.0990 | loss(seq) 0.2305 | grad 4.9399 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4520 |
[2023-10-24 04:56:08,887::train::INFO] [train] Iter 579380 | loss 0.2875 | loss(rot) 0.1494 | loss(pos) 0.1379 | loss(seq) 0.0003 | grad 2.4098 | lr 0.0000 | time_forward 3.9900 | time_backward 5.7340 |
[2023-10-24 04:56:18,460::train::INFO] [train] Iter 579381 | loss 0.1680 | loss(rot) 0.0628 | loss(pos) 0.0561 | loss(seq) 0.0491 | grad 2.1492 | lr 0.0000 | time_forward 3.9450 | time_backward 5.6250 |
[2023-10-24 04:56:26,845::train::INFO] [train] Iter 579382 | loss 0.5251 | loss(rot) 0.0398 | loss(pos) 0.4736 | loss(seq) 0.0117 | grad 6.0076 | lr 0.0000 | time_forward 3.5420 | time_backward 4.8410 |
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