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[2023-09-02 19:27:30,351::train::INFO] [train] Iter 13472 | loss 0.9406 | loss(rot) 0.6802 | loss(pos) 0.1224 | loss(seq) 0.1381 | grad 3.4629 | lr 0.0010 | time_forward 1.2600 | time_backward 1.4180 |
[2023-09-02 19:27:38,703::train::INFO] [train] Iter 13473 | loss 1.3553 | loss(rot) 0.9756 | loss(pos) 0.0809 | loss(seq) 0.2988 | grad 3.7166 | lr 0.0010 | time_forward 3.6970 | time_backward 4.6510 |
[2023-09-02 19:27:41,355::train::INFO] [train] Iter 13474 | loss 1.9986 | loss(rot) 1.8223 | loss(pos) 0.0547 | loss(seq) 0.1217 | grad 3.5124 | lr 0.0010 | time_forward 1.2030 | time_backward 1.4460 |
[2023-09-02 19:27:43,996::train::INFO] [train] Iter 13475 | loss 1.9105 | loss(rot) 0.0482 | loss(pos) 1.8600 | loss(seq) 0.0023 | grad 4.8630 | lr 0.0010 | time_forward 1.2200 | time_backward 1.4050 |
[2023-09-02 19:27:50,111::train::INFO] [train] Iter 13476 | loss 2.2129 | loss(rot) 2.0363 | loss(pos) 0.1753 | loss(seq) 0.0012 | grad 4.8331 | lr 0.0010 | time_forward 2.6190 | time_backward 3.4940 |
[2023-09-02 19:27:58,239::train::INFO] [train] Iter 13477 | loss 1.8395 | loss(rot) 1.5242 | loss(pos) 0.1629 | loss(seq) 0.1524 | grad 3.9404 | lr 0.0010 | time_forward 3.2390 | time_backward 4.8840 |
[2023-09-02 19:28:00,954::train::INFO] [train] Iter 13478 | loss 1.4747 | loss(rot) 0.7015 | loss(pos) 0.2213 | loss(seq) 0.5520 | grad 4.5829 | lr 0.0010 | time_forward 1.2430 | time_backward 1.4680 |
[2023-09-02 19:28:09,072::train::INFO] [train] Iter 13479 | loss 0.5470 | loss(rot) 0.4138 | loss(pos) 0.0738 | loss(seq) 0.0594 | grad 3.6173 | lr 0.0010 | time_forward 3.2710 | time_backward 4.8430 |
[2023-09-02 19:28:17,734::train::INFO] [train] Iter 13480 | loss 0.9959 | loss(rot) 0.6544 | loss(pos) 0.0782 | loss(seq) 0.2633 | grad 3.1358 | lr 0.0010 | time_forward 3.3150 | time_backward 5.3450 |
[2023-09-02 19:28:26,384::train::INFO] [train] Iter 13481 | loss 1.3844 | loss(rot) 0.8375 | loss(pos) 0.1202 | loss(seq) 0.4266 | grad 4.0610 | lr 0.0010 | time_forward 3.5010 | time_backward 5.1460 |
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