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[2023-09-03 01:29:40,482::train::INFO] [train] Iter 16370 | loss 0.9818 | loss(rot) 0.6348 | loss(pos) 0.2240 | loss(seq) 0.1229 | grad 7.9865 | lr 0.0010 | time_forward 1.0650 | time_backward 1.1860 |
[2023-09-03 01:29:47,749::train::INFO] [train] Iter 16371 | loss 0.9675 | loss(rot) 0.4742 | loss(pos) 0.0615 | loss(seq) 0.4318 | grad 5.1044 | lr 0.0010 | time_forward 3.1440 | time_backward 4.1190 |
[2023-09-03 01:29:50,450::train::INFO] [train] Iter 16372 | loss 0.6084 | loss(rot) 0.4337 | loss(pos) 0.1089 | loss(seq) 0.0659 | grad 4.3707 | lr 0.0010 | time_forward 1.3020 | time_backward 1.3960 |
[2023-09-03 01:29:53,628::train::INFO] [train] Iter 16373 | loss 1.7931 | loss(rot) 0.8391 | loss(pos) 0.3822 | loss(seq) 0.5718 | grad 3.2946 | lr 0.0010 | time_forward 1.3190 | time_backward 1.8570 |
[2023-09-03 01:30:01,987::train::INFO] [train] Iter 16374 | loss 2.8184 | loss(rot) 2.4167 | loss(pos) 0.2346 | loss(seq) 0.1672 | grad 4.0389 | lr 0.0010 | time_forward 3.3750 | time_backward 4.9810 |
[2023-09-03 01:30:03,541::train::INFO] [train] Iter 16375 | loss 1.2725 | loss(rot) 0.5040 | loss(pos) 0.5913 | loss(seq) 0.1772 | grad 5.3357 | lr 0.0010 | time_forward 0.6730 | time_backward 0.8770 |
[2023-09-03 01:30:06,197::train::INFO] [train] Iter 16376 | loss 1.1821 | loss(rot) 0.4035 | loss(pos) 0.1482 | loss(seq) 0.6304 | grad 3.1412 | lr 0.0010 | time_forward 1.2490 | time_backward 1.4030 |
[2023-09-03 01:30:08,786::train::INFO] [train] Iter 16377 | loss 1.8317 | loss(rot) 0.9578 | loss(pos) 0.6379 | loss(seq) 0.2361 | grad 4.9376 | lr 0.0010 | time_forward 1.1660 | time_backward 1.4210 |
[2023-09-03 01:30:11,478::train::INFO] [train] Iter 16378 | loss 0.9973 | loss(rot) 0.8896 | loss(pos) 0.1077 | loss(seq) 0.0000 | grad 3.9990 | lr 0.0010 | time_forward 1.2550 | time_backward 1.4330 |
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