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[2023-10-25 16:26:18,500::train::INFO] [train] Iter 597855 | loss 0.5137 | loss(rot) 0.1420 | loss(pos) 0.0588 | loss(seq) 0.3128 | grad 3.5485 | lr 0.0000 | time_forward 3.2620 | time_backward 4.8330 |
[2023-10-25 16:26:23,185::train::INFO] [train] Iter 597856 | loss 0.4484 | loss(rot) 0.3416 | loss(pos) 0.0280 | loss(seq) 0.0787 | grad 2.5456 | lr 0.0000 | time_forward 2.0540 | time_backward 2.6270 |
[2023-10-25 16:26:30,247::train::INFO] [train] Iter 597857 | loss 1.8884 | loss(rot) 1.5424 | loss(pos) 0.1214 | loss(seq) 0.2246 | grad 3.1808 | lr 0.0000 | time_forward 3.1200 | time_backward 3.9380 |
[2023-10-25 16:26:33,243::train::INFO] [train] Iter 597858 | loss 1.5956 | loss(rot) 1.5624 | loss(pos) 0.0306 | loss(seq) 0.0026 | grad 8.6923 | lr 0.0000 | time_forward 1.3880 | time_backward 1.6050 |
[2023-10-25 16:26:40,693::train::INFO] [train] Iter 597859 | loss 1.8428 | loss(rot) 1.4335 | loss(pos) 0.0797 | loss(seq) 0.3297 | grad 5.1234 | lr 0.0000 | time_forward 3.1360 | time_backward 3.9770 |
[2023-10-25 16:26:48,862::train::INFO] [train] Iter 597860 | loss 0.8257 | loss(rot) 0.3089 | loss(pos) 0.1382 | loss(seq) 0.3786 | grad 3.0051 | lr 0.0000 | time_forward 3.5590 | time_backward 4.6070 |
[2023-10-25 16:26:51,494::train::INFO] [train] Iter 597861 | loss 0.2655 | loss(rot) 0.0564 | loss(pos) 0.1074 | loss(seq) 0.1016 | grad 2.1582 | lr 0.0000 | time_forward 1.2290 | time_backward 1.4000 |
[2023-10-25 16:26:59,258::train::INFO] [train] Iter 597862 | loss 3.0198 | loss(rot) 0.0042 | loss(pos) 3.0156 | loss(seq) 0.0000 | grad 14.1325 | lr 0.0000 | time_forward 3.2490 | time_backward 4.5090 |
[2023-10-25 16:27:06,999::train::INFO] [train] Iter 597863 | loss 0.5865 | loss(rot) 0.2730 | loss(pos) 0.0421 | loss(seq) 0.2713 | grad 3.3921 | lr 0.0000 | time_forward 3.2020 | time_backward 4.5340 |
[2023-10-25 16:27:14,802::train::INFO] [train] Iter 597864 | loss 0.5033 | loss(rot) 0.1145 | loss(pos) 0.3435 | loss(seq) 0.0452 | grad 5.7941 | lr 0.0000 | time_forward 3.2820 | time_backward 4.5180 |
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