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[2023-10-24 07:14:57,166::train::INFO] [train] Iter 580572 | loss 0.0748 | loss(rot) 0.0621 | loss(pos) 0.0127 | loss(seq) 0.0001 | grad 1.3080 | lr 0.0000 | time_forward 3.4330 | time_backward 4.6880 |
[2023-10-24 07:15:06,666::train::INFO] [train] Iter 580573 | loss 0.7094 | loss(rot) 0.3698 | loss(pos) 0.0751 | loss(seq) 0.2645 | grad 3.1079 | lr 0.0000 | time_forward 3.9040 | time_backward 5.5920 |
[2023-10-24 07:15:16,095::train::INFO] [train] Iter 580574 | loss 0.5264 | loss(rot) 0.1275 | loss(pos) 0.2066 | loss(seq) 0.1922 | grad 2.7252 | lr 0.0000 | time_forward 3.7260 | time_backward 5.7000 |
[2023-10-24 07:15:24,200::train::INFO] [train] Iter 580575 | loss 0.8029 | loss(rot) 0.5054 | loss(pos) 0.0360 | loss(seq) 0.2616 | grad 3.7574 | lr 0.0000 | time_forward 3.4380 | time_backward 4.6630 |
[2023-10-24 07:15:32,423::train::INFO] [train] Iter 580576 | loss 0.2346 | loss(rot) 0.0642 | loss(pos) 0.1556 | loss(seq) 0.0148 | grad 4.3178 | lr 0.0000 | time_forward 3.3570 | time_backward 4.8640 |
[2023-10-24 07:15:40,351::train::INFO] [train] Iter 580577 | loss 1.6271 | loss(rot) 1.0729 | loss(pos) 0.1005 | loss(seq) 0.4537 | grad 7.2433 | lr 0.0000 | time_forward 3.3690 | time_backward 4.5560 |
[2023-10-24 07:15:49,709::train::INFO] [train] Iter 580578 | loss 0.4508 | loss(rot) 0.1377 | loss(pos) 0.0782 | loss(seq) 0.2349 | grad 2.8153 | lr 0.0000 | time_forward 3.8350 | time_backward 5.5200 |
[2023-10-24 07:15:52,520::train::INFO] [train] Iter 580579 | loss 0.8046 | loss(rot) 0.3949 | loss(pos) 0.0543 | loss(seq) 0.3554 | grad 2.8772 | lr 0.0000 | time_forward 1.3000 | time_backward 1.5080 |
[2023-10-24 07:16:01,344::train::INFO] [train] Iter 580580 | loss 0.2870 | loss(rot) 0.0596 | loss(pos) 0.1359 | loss(seq) 0.0915 | grad 2.8845 | lr 0.0000 | time_forward 3.7310 | time_backward 5.0620 |
[2023-10-24 07:16:09,400::train::INFO] [train] Iter 580581 | loss 0.9273 | loss(rot) 0.5417 | loss(pos) 0.0584 | loss(seq) 0.3272 | grad 18.3323 | lr 0.0000 | time_forward 3.4260 | time_backward 4.6270 |
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