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[2023-09-02 17:39:51,709::train::INFO] [train] Iter 12573 | loss 1.8867 | loss(rot) 1.1342 | loss(pos) 0.2671 | loss(seq) 0.4854 | grad 7.5164 | lr 0.0010 | time_forward 3.2890 | time_backward 4.3700 |
[2023-09-02 17:40:00,082::train::INFO] [train] Iter 12574 | loss 1.3375 | loss(rot) 0.8523 | loss(pos) 0.2030 | loss(seq) 0.2822 | grad 5.2994 | lr 0.0010 | time_forward 3.6210 | time_backward 4.7470 |
[2023-09-02 17:40:08,069::train::INFO] [train] Iter 12575 | loss 0.9669 | loss(rot) 0.6087 | loss(pos) 0.3582 | loss(seq) 0.0000 | grad 9.7169 | lr 0.0010 | time_forward 3.5170 | time_backward 4.4680 |
[2023-09-02 17:40:17,058::train::INFO] [train] Iter 12576 | loss 1.1126 | loss(rot) 0.2496 | loss(pos) 0.5212 | loss(seq) 0.3418 | grad 7.7449 | lr 0.0010 | time_forward 3.7110 | time_backward 5.2750 |
[2023-09-02 17:40:28,031::train::INFO] [train] Iter 12577 | loss 2.0638 | loss(rot) 1.5082 | loss(pos) 0.2278 | loss(seq) 0.3278 | grad 4.1738 | lr 0.0010 | time_forward 4.6180 | time_backward 6.3520 |
[2023-09-02 17:40:30,890::train::INFO] [train] Iter 12578 | loss 2.6802 | loss(rot) 1.7357 | loss(pos) 0.5145 | loss(seq) 0.4300 | grad 4.7391 | lr 0.0010 | time_forward 1.3370 | time_backward 1.5190 |
[2023-09-02 17:40:40,002::train::INFO] [train] Iter 12579 | loss 2.2996 | loss(rot) 2.0245 | loss(pos) 0.2319 | loss(seq) 0.0432 | grad 8.0934 | lr 0.0010 | time_forward 4.1590 | time_backward 4.9270 |
[2023-09-02 17:40:46,984::train::INFO] [train] Iter 12580 | loss 1.0954 | loss(rot) 0.6730 | loss(pos) 0.2292 | loss(seq) 0.1932 | grad 3.4997 | lr 0.0010 | time_forward 2.9200 | time_backward 4.0590 |
[2023-09-02 17:40:49,479::train::INFO] [train] Iter 12581 | loss 1.5936 | loss(rot) 1.0853 | loss(pos) 0.1250 | loss(seq) 0.3833 | grad 5.1808 | lr 0.0010 | time_forward 1.2340 | time_backward 1.2570 |
[2023-09-02 17:40:55,706::train::INFO] [train] Iter 12582 | loss 1.7181 | loss(rot) 0.9171 | loss(pos) 0.1940 | loss(seq) 0.6071 | grad 5.0010 | lr 0.0010 | time_forward 2.4520 | time_backward 3.7410 |
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