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[2023-10-25 09:45:36,865::train::INFO] [train] Iter 594458 | loss 0.7857 | loss(rot) 0.0194 | loss(pos) 0.7629 | loss(seq) 0.0034 | grad 10.6837 | lr 0.0000 | time_forward 3.7880 | time_backward 5.1980 |
[2023-10-25 09:45:39,554::train::INFO] [train] Iter 594459 | loss 0.2286 | loss(rot) 0.0946 | loss(pos) 0.0652 | loss(seq) 0.0688 | grad 2.9849 | lr 0.0000 | time_forward 1.2470 | time_backward 1.4380 |
[2023-10-25 09:45:48,184::train::INFO] [train] Iter 594460 | loss 0.7804 | loss(rot) 0.0971 | loss(pos) 0.6730 | loss(seq) 0.0103 | grad 4.3426 | lr 0.0000 | time_forward 3.6020 | time_backward 4.9910 |
[2023-10-25 09:45:51,078::train::INFO] [train] Iter 594461 | loss 0.4965 | loss(rot) 0.1341 | loss(pos) 0.2776 | loss(seq) 0.0848 | grad 3.9503 | lr 0.0000 | time_forward 1.3220 | time_backward 1.5680 |
[2023-10-25 09:45:57,811::train::INFO] [train] Iter 594462 | loss 0.7111 | loss(rot) 0.3863 | loss(pos) 0.1096 | loss(seq) 0.2153 | grad 3.4290 | lr 0.0000 | time_forward 2.8160 | time_backward 3.9150 |
[2023-10-25 09:46:06,970::train::INFO] [train] Iter 594463 | loss 0.2519 | loss(rot) 0.0191 | loss(pos) 0.2231 | loss(seq) 0.0096 | grad 4.1181 | lr 0.0000 | time_forward 3.8720 | time_backward 5.2830 |
[2023-10-25 09:46:15,740::train::INFO] [train] Iter 594464 | loss 0.4319 | loss(rot) 0.1252 | loss(pos) 0.0197 | loss(seq) 0.2871 | grad 2.8310 | lr 0.0000 | time_forward 3.7010 | time_backward 5.0650 |
[2023-10-25 09:46:18,689::train::INFO] [train] Iter 594465 | loss 0.1015 | loss(rot) 0.0759 | loss(pos) 0.0190 | loss(seq) 0.0065 | grad 1.3304 | lr 0.0000 | time_forward 1.3100 | time_backward 1.6360 |
[2023-10-25 09:46:27,967::train::INFO] [train] Iter 594466 | loss 0.3949 | loss(rot) 0.2290 | loss(pos) 0.0685 | loss(seq) 0.0974 | grad 4.1149 | lr 0.0000 | time_forward 3.9140 | time_backward 5.3620 |
[2023-10-25 09:46:30,946::train::INFO] [train] Iter 594467 | loss 0.5309 | loss(rot) 0.2297 | loss(pos) 0.0620 | loss(seq) 0.2393 | grad 2.5462 | lr 0.0000 | time_forward 1.3120 | time_backward 1.6630 |
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