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[2023-10-22 21:19:04,924::train::INFO] [train] Iter 562090 | loss 0.4547 | loss(rot) 0.1298 | loss(pos) 0.0359 | loss(seq) 0.2890 | grad 2.1977 | lr 0.0000 | time_forward 3.5940 | time_backward 5.1820 |
[2023-10-22 21:19:07,609::train::INFO] [train] Iter 562091 | loss 0.1228 | loss(rot) 0.0807 | loss(pos) 0.0360 | loss(seq) 0.0061 | grad 1.5415 | lr 0.0000 | time_forward 1.2740 | time_backward 1.4080 |
[2023-10-22 21:19:16,336::train::INFO] [train] Iter 562092 | loss 0.7238 | loss(rot) 0.6825 | loss(pos) 0.0252 | loss(seq) 0.0161 | grad 5.0219 | lr 0.0000 | time_forward 3.5780 | time_backward 5.1230 |
[2023-10-22 21:19:25,235::train::INFO] [train] Iter 562093 | loss 0.7932 | loss(rot) 0.1482 | loss(pos) 0.6266 | loss(seq) 0.0184 | grad 8.9226 | lr 0.0000 | time_forward 3.6220 | time_backward 5.2750 |
[2023-10-22 21:19:33,297::train::INFO] [train] Iter 562094 | loss 1.2503 | loss(rot) 0.6693 | loss(pos) 0.3477 | loss(seq) 0.2333 | grad 4.0648 | lr 0.0000 | time_forward 3.4560 | time_backward 4.6030 |
[2023-10-22 21:19:40,956::train::INFO] [train] Iter 562095 | loss 0.3173 | loss(rot) 0.2402 | loss(pos) 0.0382 | loss(seq) 0.0389 | grad 2.4593 | lr 0.0000 | time_forward 3.2580 | time_backward 4.3970 |
[2023-10-22 21:19:49,820::train::INFO] [train] Iter 562096 | loss 1.7392 | loss(rot) 1.0431 | loss(pos) 0.2634 | loss(seq) 0.4327 | grad 4.0910 | lr 0.0000 | time_forward 3.5720 | time_backward 5.2890 |
[2023-10-22 21:19:57,147::train::INFO] [train] Iter 562097 | loss 0.8396 | loss(rot) 0.1480 | loss(pos) 0.0971 | loss(seq) 0.5945 | grad 2.7280 | lr 0.0000 | time_forward 3.0570 | time_backward 4.2680 |
[2023-10-22 21:20:00,642::train::INFO] [train] Iter 562098 | loss 2.3650 | loss(rot) 2.3331 | loss(pos) 0.0311 | loss(seq) 0.0007 | grad 3.3573 | lr 0.0000 | time_forward 1.6200 | time_backward 1.8720 |
[2023-10-22 21:20:08,725::train::INFO] [train] Iter 562099 | loss 1.9190 | loss(rot) 1.7906 | loss(pos) 0.1025 | loss(seq) 0.0259 | grad 37.2362 | lr 0.0000 | time_forward 3.2460 | time_backward 4.8340 |
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