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[2023-09-02 05:43:15,750::train::INFO] [train] Iter 06579 | loss 2.1095 | loss(rot) 1.4712 | loss(pos) 0.2043 | loss(seq) 0.4340 | grad 7.0926 | lr 0.0010 | time_forward 4.0340 | time_backward 6.0010 |
[2023-09-02 05:43:25,950::train::INFO] [train] Iter 06580 | loss 2.1441 | loss(rot) 0.9791 | loss(pos) 0.6000 | loss(seq) 0.5650 | grad 3.4410 | lr 0.0010 | time_forward 4.2340 | time_backward 5.9620 |
[2023-09-02 05:43:34,673::train::INFO] [train] Iter 06581 | loss 0.8416 | loss(rot) 0.2231 | loss(pos) 0.3612 | loss(seq) 0.2572 | grad 2.8755 | lr 0.0010 | time_forward 3.6920 | time_backward 5.0280 |
[2023-09-02 05:43:44,243::train::INFO] [train] Iter 06582 | loss 3.0965 | loss(rot) 2.8289 | loss(pos) 0.2676 | loss(seq) 0.0000 | grad 3.9582 | lr 0.0010 | time_forward 4.0450 | time_backward 5.5210 |
[2023-09-02 05:43:55,143::train::INFO] [train] Iter 06583 | loss 1.5043 | loss(rot) 0.8096 | loss(pos) 0.3165 | loss(seq) 0.3783 | grad 2.8329 | lr 0.0010 | time_forward 4.3140 | time_backward 6.5820 |
[2023-09-02 05:44:04,153::train::INFO] [train] Iter 06584 | loss 1.2987 | loss(rot) 0.7168 | loss(pos) 0.2727 | loss(seq) 0.3092 | grad 4.0594 | lr 0.0010 | time_forward 3.9260 | time_backward 5.0800 |
[2023-09-02 05:44:13,629::train::INFO] [train] Iter 06585 | loss 2.0542 | loss(rot) 1.5316 | loss(pos) 0.2363 | loss(seq) 0.2863 | grad 5.3039 | lr 0.0010 | time_forward 4.0990 | time_backward 5.3740 |
[2023-09-02 05:44:16,453::train::INFO] [train] Iter 06586 | loss 2.5813 | loss(rot) 1.5773 | loss(pos) 0.5714 | loss(seq) 0.4326 | grad 3.2421 | lr 0.0010 | time_forward 1.3440 | time_backward 1.4750 |
[2023-09-02 05:44:26,647::train::INFO] [train] Iter 06587 | loss 1.2889 | loss(rot) 0.1803 | loss(pos) 1.0449 | loss(seq) 0.0637 | grad 8.1223 | lr 0.0010 | time_forward 4.4840 | time_backward 5.7070 |
[2023-09-02 05:44:29,980::train::INFO] [train] Iter 06588 | loss 2.6112 | loss(rot) 2.2761 | loss(pos) 0.3351 | loss(seq) 0.0000 | grad 4.9446 | lr 0.0010 | time_forward 1.8820 | time_backward 1.4470 |
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