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[2023-10-25 09:08:18,845::train::INFO] [train] Iter 594158 | loss 0.7873 | loss(rot) 0.4585 | loss(pos) 0.1015 | loss(seq) 0.2273 | grad 3.1146 | lr 0.0000 | time_forward 3.9100 | time_backward 5.4110 |
[2023-10-25 09:08:27,868::train::INFO] [train] Iter 594159 | loss 0.7755 | loss(rot) 0.3839 | loss(pos) 0.0827 | loss(seq) 0.3089 | grad 3.7759 | lr 0.0000 | time_forward 3.8340 | time_backward 5.1840 |
[2023-10-25 09:08:35,136::train::INFO] [train] Iter 594160 | loss 0.3052 | loss(rot) 0.0777 | loss(pos) 0.0623 | loss(seq) 0.1653 | grad 3.5901 | lr 0.0000 | time_forward 3.1650 | time_backward 4.0990 |
[2023-10-25 09:08:42,029::train::INFO] [train] Iter 594161 | loss 1.4085 | loss(rot) 1.1108 | loss(pos) 0.0299 | loss(seq) 0.2678 | grad 3.6544 | lr 0.0000 | time_forward 2.8710 | time_backward 4.0190 |
[2023-10-25 09:08:51,986::train::INFO] [train] Iter 594162 | loss 1.0256 | loss(rot) 0.8346 | loss(pos) 0.0482 | loss(seq) 0.1428 | grad 3.8262 | lr 0.0000 | time_forward 4.1440 | time_backward 5.8100 |
[2023-10-25 09:08:54,879::train::INFO] [train] Iter 594163 | loss 1.8381 | loss(rot) 1.1644 | loss(pos) 0.0762 | loss(seq) 0.5975 | grad 7.8536 | lr 0.0000 | time_forward 1.3960 | time_backward 1.4940 |
[2023-10-25 09:09:04,553::train::INFO] [train] Iter 594164 | loss 0.8308 | loss(rot) 0.5749 | loss(pos) 0.0327 | loss(seq) 0.2232 | grad 3.2473 | lr 0.0000 | time_forward 3.9350 | time_backward 5.7010 |
[2023-10-25 09:09:13,168::train::INFO] [train] Iter 594165 | loss 0.9312 | loss(rot) 0.0176 | loss(pos) 0.9110 | loss(seq) 0.0026 | grad 7.5903 | lr 0.0000 | time_forward 3.7110 | time_backward 4.8990 |
[2023-10-25 09:09:18,659::train::INFO] [train] Iter 594166 | loss 2.4041 | loss(rot) 1.7912 | loss(pos) 0.1842 | loss(seq) 0.4287 | grad 13.9392 | lr 0.0000 | time_forward 2.3460 | time_backward 3.1410 |
[2023-10-25 09:09:26,460::train::INFO] [train] Iter 594167 | loss 2.2206 | loss(rot) 0.0658 | loss(pos) 2.1530 | loss(seq) 0.0018 | grad 21.9926 | lr 0.0000 | time_forward 3.3500 | time_backward 4.4490 |
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