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[2023-10-24 15:04:12,304::train::INFO] [train] Iter 584468 | loss 0.5723 | loss(rot) 0.4468 | loss(pos) 0.0326 | loss(seq) 0.0930 | grad 3.9058 | lr 0.0000 | time_forward 2.9980 | time_backward 3.8030 |
[2023-10-24 15:04:20,291::train::INFO] [train] Iter 584469 | loss 0.4022 | loss(rot) 0.3094 | loss(pos) 0.0251 | loss(seq) 0.0676 | grad 8.2372 | lr 0.0000 | time_forward 3.3420 | time_backward 4.6420 |
[2023-10-24 15:04:27,628::train::INFO] [train] Iter 584470 | loss 0.7471 | loss(rot) 0.0242 | loss(pos) 0.7210 | loss(seq) 0.0019 | grad 14.5551 | lr 0.0000 | time_forward 3.1990 | time_backward 4.1340 |
[2023-10-24 15:04:34,713::train::INFO] [train] Iter 584471 | loss 0.2805 | loss(rot) 0.0704 | loss(pos) 0.0575 | loss(seq) 0.1525 | grad 2.6888 | lr 0.0000 | time_forward 3.1490 | time_backward 3.9320 |
[2023-10-24 15:04:42,006::train::INFO] [train] Iter 584472 | loss 1.3693 | loss(rot) 0.5436 | loss(pos) 0.0481 | loss(seq) 0.7776 | grad 4.7825 | lr 0.0000 | time_forward 3.1980 | time_backward 4.0910 |
[2023-10-24 15:04:49,349::train::INFO] [train] Iter 584473 | loss 0.4500 | loss(rot) 0.0698 | loss(pos) 0.0522 | loss(seq) 0.3280 | grad 2.9849 | lr 0.0000 | time_forward 3.0540 | time_backward 4.2860 |
[2023-10-24 15:04:57,709::train::INFO] [train] Iter 584474 | loss 0.3684 | loss(rot) 0.2786 | loss(pos) 0.0238 | loss(seq) 0.0661 | grad 3.4366 | lr 0.0000 | time_forward 3.6820 | time_backward 4.6750 |
[2023-10-24 15:05:06,735::train::INFO] [train] Iter 584475 | loss 0.4894 | loss(rot) 0.1305 | loss(pos) 0.3199 | loss(seq) 0.0390 | grad 4.8118 | lr 0.0000 | time_forward 3.6610 | time_backward 5.3610 |
[2023-10-24 15:05:14,717::train::INFO] [train] Iter 584476 | loss 1.1539 | loss(rot) 1.0780 | loss(pos) 0.0759 | loss(seq) 0.0000 | grad 9.7680 | lr 0.0000 | time_forward 3.4630 | time_backward 4.5170 |
[2023-10-24 15:05:21,576::train::INFO] [train] Iter 584477 | loss 0.9532 | loss(rot) 0.5673 | loss(pos) 0.0909 | loss(seq) 0.2950 | grad 4.0294 | lr 0.0000 | time_forward 2.9560 | time_backward 3.8990 |
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