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[2023-10-23 08:11:38,449::train::INFO] [train] Iter 568484 | loss 0.5185 | loss(rot) 0.1258 | loss(pos) 0.3840 | loss(seq) 0.0087 | grad 4.7544 | lr 0.0000 | time_forward 3.3980 | time_backward 4.6370 |
[2023-10-23 08:11:43,896::train::INFO] [train] Iter 568485 | loss 0.1336 | loss(rot) 0.1056 | loss(pos) 0.0276 | loss(seq) 0.0004 | grad 1.9587 | lr 0.0000 | time_forward 2.3060 | time_backward 3.1370 |
[2023-10-23 08:11:51,431::train::INFO] [train] Iter 568486 | loss 0.5579 | loss(rot) 0.2339 | loss(pos) 0.0400 | loss(seq) 0.2840 | grad 2.2289 | lr 0.0000 | time_forward 3.1820 | time_backward 4.3410 |
[2023-10-23 08:11:59,446::train::INFO] [train] Iter 568487 | loss 0.5870 | loss(rot) 0.5629 | loss(pos) 0.0239 | loss(seq) 0.0002 | grad 3.1735 | lr 0.0000 | time_forward 3.3260 | time_backward 4.6860 |
[2023-10-23 08:12:06,535::train::INFO] [train] Iter 568488 | loss 0.2535 | loss(rot) 0.1129 | loss(pos) 0.0408 | loss(seq) 0.0999 | grad 2.6057 | lr 0.0000 | time_forward 3.0850 | time_backward 4.0010 |
[2023-10-23 08:12:13,604::train::INFO] [train] Iter 568489 | loss 0.3181 | loss(rot) 0.1431 | loss(pos) 0.0391 | loss(seq) 0.1359 | grad 3.5923 | lr 0.0000 | time_forward 3.0560 | time_backward 4.0110 |
[2023-10-23 08:12:21,797::train::INFO] [train] Iter 568490 | loss 1.1980 | loss(rot) 0.7843 | loss(pos) 0.1479 | loss(seq) 0.2658 | grad 5.7884 | lr 0.0000 | time_forward 3.3090 | time_backward 4.8800 |
[2023-10-23 08:12:28,405::train::INFO] [train] Iter 568491 | loss 0.2138 | loss(rot) 0.1348 | loss(pos) 0.0236 | loss(seq) 0.0555 | grad 2.4140 | lr 0.0000 | time_forward 2.8210 | time_backward 3.7850 |
[2023-10-23 08:12:35,230::train::INFO] [train] Iter 568492 | loss 1.7845 | loss(rot) 0.0186 | loss(pos) 1.5367 | loss(seq) 0.2293 | grad 9.6985 | lr 0.0000 | time_forward 2.9510 | time_backward 3.8710 |
[2023-10-23 08:12:43,303::train::INFO] [train] Iter 568493 | loss 0.5249 | loss(rot) 0.4880 | loss(pos) 0.0352 | loss(seq) 0.0017 | grad 4.0853 | lr 0.0000 | time_forward 3.3210 | time_backward 4.7490 |
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