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[2023-10-24 09:40:19,263::train::INFO] [train] Iter 581771 | loss 0.5964 | loss(rot) 0.4424 | loss(pos) 0.0292 | loss(seq) 0.1249 | grad 2.3983 | lr 0.0000 | time_forward 1.5560 | time_backward 1.8560 |
[2023-10-24 09:40:29,035::train::INFO] [train] Iter 581772 | loss 1.2284 | loss(rot) 1.1806 | loss(pos) 0.0333 | loss(seq) 0.0145 | grad 3.7837 | lr 0.0000 | time_forward 3.9940 | time_backward 5.7630 |
[2023-10-24 09:40:38,690::train::INFO] [train] Iter 581773 | loss 1.6444 | loss(rot) 1.6122 | loss(pos) 0.0320 | loss(seq) 0.0003 | grad 4.0391 | lr 0.0000 | time_forward 3.9150 | time_backward 5.7380 |
[2023-10-24 09:40:46,826::train::INFO] [train] Iter 581774 | loss 4.5724 | loss(rot) 0.0049 | loss(pos) 4.5675 | loss(seq) 0.0000 | grad 36.7735 | lr 0.0000 | time_forward 3.4340 | time_backward 4.6980 |
[2023-10-24 09:40:49,696::train::INFO] [train] Iter 581775 | loss 0.3560 | loss(rot) 0.2639 | loss(pos) 0.0150 | loss(seq) 0.0771 | grad 74.3932 | lr 0.0000 | time_forward 1.4180 | time_backward 1.4490 |
[2023-10-24 09:40:56,159::train::INFO] [train] Iter 581776 | loss 0.3629 | loss(rot) 0.3087 | loss(pos) 0.0535 | loss(seq) 0.0006 | grad 4.4693 | lr 0.0000 | time_forward 2.7590 | time_backward 3.7020 |
[2023-10-24 09:41:06,131::train::INFO] [train] Iter 581777 | loss 0.5004 | loss(rot) 0.4443 | loss(pos) 0.0293 | loss(seq) 0.0268 | grad 9.6579 | lr 0.0000 | time_forward 4.0420 | time_backward 5.9160 |
[2023-10-24 09:41:16,104::train::INFO] [train] Iter 581778 | loss 1.3141 | loss(rot) 1.2781 | loss(pos) 0.0227 | loss(seq) 0.0133 | grad 4.8528 | lr 0.0000 | time_forward 4.1540 | time_backward 5.8160 |
[2023-10-24 09:41:25,793::train::INFO] [train] Iter 581779 | loss 0.8219 | loss(rot) 0.6078 | loss(pos) 0.0490 | loss(seq) 0.1651 | grad 16.2936 | lr 0.0000 | time_forward 3.9570 | time_backward 5.7290 |
[2023-10-24 09:41:35,510::train::INFO] [train] Iter 581780 | loss 0.5945 | loss(rot) 0.3636 | loss(pos) 0.0471 | loss(seq) 0.1838 | grad 3.3879 | lr 0.0000 | time_forward 3.9330 | time_backward 5.7790 |
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