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[2023-10-22 17:31:23,080::train::INFO] [train] Iter 560092 | loss 0.2681 | loss(rot) 0.1536 | loss(pos) 0.0176 | loss(seq) 0.0969 | grad 2.4531 | lr 0.0000 | time_forward 4.0790 | time_backward 5.7570 |
[2023-10-22 17:31:30,984::train::INFO] [train] Iter 560093 | loss 1.0252 | loss(rot) 0.0092 | loss(pos) 1.0151 | loss(seq) 0.0010 | grad 9.6099 | lr 0.0000 | time_forward 3.3670 | time_backward 4.5340 |
[2023-10-22 17:31:40,279::train::INFO] [train] Iter 560094 | loss 0.9357 | loss(rot) 0.4911 | loss(pos) 0.0900 | loss(seq) 0.3546 | grad 5.0911 | lr 0.0000 | time_forward 3.8930 | time_backward 5.3980 |
[2023-10-22 17:31:49,285::train::INFO] [train] Iter 560095 | loss 0.4180 | loss(rot) 0.0991 | loss(pos) 0.1649 | loss(seq) 0.1539 | grad 2.8607 | lr 0.0000 | time_forward 3.8730 | time_backward 5.1300 |
[2023-10-22 17:31:55,069::train::INFO] [train] Iter 560096 | loss 0.1670 | loss(rot) 0.0466 | loss(pos) 0.0660 | loss(seq) 0.0545 | grad 3.4080 | lr 0.0000 | time_forward 2.4940 | time_backward 3.2870 |
[2023-10-22 17:31:57,853::train::INFO] [train] Iter 560097 | loss 0.2125 | loss(rot) 0.1379 | loss(pos) 0.0227 | loss(seq) 0.0519 | grad 2.5176 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4520 |
[2023-10-22 17:32:04,930::train::INFO] [train] Iter 560098 | loss 0.4306 | loss(rot) 0.1528 | loss(pos) 0.0562 | loss(seq) 0.2215 | grad 2.3378 | lr 0.0000 | time_forward 3.0300 | time_backward 4.0090 |
[2023-10-22 17:32:14,548::train::INFO] [train] Iter 560099 | loss 1.0907 | loss(rot) 1.0056 | loss(pos) 0.0321 | loss(seq) 0.0529 | grad 8.1231 | lr 0.0000 | time_forward 3.9010 | time_backward 5.7130 |
[2023-10-22 17:32:15,138::train::INFO] [train] Iter 560100 | loss 1.4357 | loss(rot) 0.7604 | loss(pos) 0.1560 | loss(seq) 0.5193 | grad 20.0355 | lr 0.0000 | time_forward 0.2350 | time_backward 0.3520 |
[2023-10-22 17:32:18,027::train::INFO] [train] Iter 560101 | loss 0.4242 | loss(rot) 0.3572 | loss(pos) 0.0150 | loss(seq) 0.0520 | grad 2.3541 | lr 0.0000 | time_forward 1.3150 | time_backward 1.5700 |
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