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[2023-10-24 02:45:49,729::train::INFO] [train] Iter 578274 | loss 1.8698 | loss(rot) 1.8372 | loss(pos) 0.0294 | loss(seq) 0.0031 | grad 4.7351 | lr 0.0000 | time_forward 3.4220 | time_backward 4.6730 |
[2023-10-24 02:45:58,236::train::INFO] [train] Iter 578275 | loss 0.2066 | loss(rot) 0.0527 | loss(pos) 0.1493 | loss(seq) 0.0046 | grad 3.1206 | lr 0.0000 | time_forward 3.5760 | time_backward 4.9280 |
[2023-10-24 02:46:07,136::train::INFO] [train] Iter 578276 | loss 0.4835 | loss(rot) 0.1638 | loss(pos) 0.0475 | loss(seq) 0.2723 | grad 2.1205 | lr 0.0000 | time_forward 3.7400 | time_backward 5.1570 |
[2023-10-24 02:46:16,714::train::INFO] [train] Iter 578277 | loss 0.5749 | loss(rot) 0.0579 | loss(pos) 0.5062 | loss(seq) 0.0108 | grad 5.7318 | lr 0.0000 | time_forward 3.8970 | time_backward 5.6770 |
[2023-10-24 02:46:25,199::train::INFO] [train] Iter 578278 | loss 0.1889 | loss(rot) 0.0446 | loss(pos) 0.1391 | loss(seq) 0.0052 | grad 3.6487 | lr 0.0000 | time_forward 3.5560 | time_backward 4.9270 |
[2023-10-24 02:46:34,802::train::INFO] [train] Iter 578279 | loss 1.1079 | loss(rot) 0.5666 | loss(pos) 0.1453 | loss(seq) 0.3961 | grad 3.6720 | lr 0.0000 | time_forward 4.0690 | time_backward 5.5310 |
[2023-10-24 02:46:44,375::train::INFO] [train] Iter 578280 | loss 0.4200 | loss(rot) 0.3347 | loss(pos) 0.0455 | loss(seq) 0.0398 | grad 13.7555 | lr 0.0000 | time_forward 3.9440 | time_backward 5.6250 |
[2023-10-24 02:46:53,107::train::INFO] [train] Iter 578281 | loss 0.5624 | loss(rot) 0.1408 | loss(pos) 0.0423 | loss(seq) 0.3793 | grad 2.8848 | lr 0.0000 | time_forward 3.6830 | time_backward 5.0470 |
[2023-10-24 02:47:01,826::train::INFO] [train] Iter 578282 | loss 0.5688 | loss(rot) 0.3939 | loss(pos) 0.0151 | loss(seq) 0.1598 | grad 4.5804 | lr 0.0000 | time_forward 3.6990 | time_backward 5.0170 |
[2023-10-24 02:47:09,518::train::INFO] [train] Iter 578283 | loss 2.3882 | loss(rot) 2.3595 | loss(pos) 0.0287 | loss(seq) 0.0000 | grad 4.0340 | lr 0.0000 | time_forward 3.2230 | time_backward 4.4650 |
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