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[2023-10-24 15:14:34,981::train::INFO] [train] Iter 584568 | loss 0.1052 | loss(rot) 0.0564 | loss(pos) 0.0488 | loss(seq) 0.0000 | grad 2.1033 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4340 |
[2023-10-24 15:14:43,202::train::INFO] [train] Iter 584569 | loss 1.2025 | loss(rot) 0.6992 | loss(pos) 0.1839 | loss(seq) 0.3194 | grad 4.1274 | lr 0.0000 | time_forward 3.4930 | time_backward 4.6820 |
[2023-10-24 15:14:51,087::train::INFO] [train] Iter 584570 | loss 0.5191 | loss(rot) 0.1797 | loss(pos) 0.0677 | loss(seq) 0.2716 | grad 2.9127 | lr 0.0000 | time_forward 3.3300 | time_backward 4.5530 |
[2023-10-24 15:14:59,684::train::INFO] [train] Iter 584571 | loss 0.4885 | loss(rot) 0.2094 | loss(pos) 0.2657 | loss(seq) 0.0134 | grad 5.5180 | lr 0.0000 | time_forward 3.6180 | time_backward 4.9750 |
[2023-10-24 15:15:02,438::train::INFO] [train] Iter 584572 | loss 0.1406 | loss(rot) 0.0456 | loss(pos) 0.0740 | loss(seq) 0.0210 | grad 2.8581 | lr 0.0000 | time_forward 1.3200 | time_backward 1.4300 |
[2023-10-24 15:15:11,438::train::INFO] [train] Iter 584573 | loss 0.2493 | loss(rot) 0.1501 | loss(pos) 0.0493 | loss(seq) 0.0499 | grad 2.2565 | lr 0.0000 | time_forward 3.8600 | time_backward 5.1370 |
[2023-10-24 15:15:14,301::train::INFO] [train] Iter 584574 | loss 1.1798 | loss(rot) 0.6398 | loss(pos) 0.3584 | loss(seq) 0.1815 | grad 5.8233 | lr 0.0000 | time_forward 1.3250 | time_backward 1.5350 |
[2023-10-24 15:15:16,625::train::INFO] [train] Iter 584575 | loss 0.4691 | loss(rot) 0.1269 | loss(pos) 0.0819 | loss(seq) 0.2603 | grad 2.7981 | lr 0.0000 | time_forward 1.0340 | time_backward 1.2540 |
[2023-10-24 15:15:25,608::train::INFO] [train] Iter 584576 | loss 1.5079 | loss(rot) 1.3687 | loss(pos) 0.0833 | loss(seq) 0.0559 | grad 5.3427 | lr 0.0000 | time_forward 3.8140 | time_backward 5.1670 |
[2023-10-24 15:15:32,562::train::INFO] [train] Iter 584577 | loss 0.1664 | loss(rot) 0.0344 | loss(pos) 0.1069 | loss(seq) 0.0250 | grad 3.3098 | lr 0.0000 | time_forward 2.9300 | time_backward 4.0190 |
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