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[2023-10-25 10:46:26,637::train::INFO] [train] Iter 594958 | loss 0.3018 | loss(rot) 0.0359 | loss(pos) 0.2604 | loss(seq) 0.0055 | grad 4.9601 | lr 0.0000 | time_forward 3.3590 | time_backward 4.6610 |
[2023-10-25 10:46:34,691::train::INFO] [train] Iter 594959 | loss 2.3331 | loss(rot) 1.8685 | loss(pos) 0.1242 | loss(seq) 0.3403 | grad 8.4856 | lr 0.0000 | time_forward 3.4080 | time_backward 4.6420 |
[2023-10-25 10:46:37,007::train::INFO] [train] Iter 594960 | loss 0.4956 | loss(rot) 0.0398 | loss(pos) 0.3572 | loss(seq) 0.0987 | grad 5.0665 | lr 0.0000 | time_forward 1.0640 | time_backward 1.2480 |
[2023-10-25 10:46:45,516::train::INFO] [train] Iter 594961 | loss 0.4392 | loss(rot) 0.0641 | loss(pos) 0.0838 | loss(seq) 0.2913 | grad 3.6907 | lr 0.0000 | time_forward 3.6220 | time_backward 4.8850 |
[2023-10-25 10:46:48,331::train::INFO] [train] Iter 594962 | loss 0.5570 | loss(rot) 0.3737 | loss(pos) 0.0320 | loss(seq) 0.1512 | grad 5.0255 | lr 0.0000 | time_forward 1.3410 | time_backward 1.4700 |
[2023-10-25 10:46:53,969::train::INFO] [train] Iter 594963 | loss 0.3371 | loss(rot) 0.0504 | loss(pos) 0.1813 | loss(seq) 0.1054 | grad 4.9260 | lr 0.0000 | time_forward 2.4380 | time_backward 3.1970 |
[2023-10-25 10:47:01,583::train::INFO] [train] Iter 594964 | loss 0.1938 | loss(rot) 0.0759 | loss(pos) 0.0198 | loss(seq) 0.0980 | grad 1.7220 | lr 0.0000 | time_forward 3.2590 | time_backward 4.3410 |
[2023-10-25 10:47:10,831::train::INFO] [train] Iter 594965 | loss 1.5045 | loss(rot) 1.2567 | loss(pos) 0.0296 | loss(seq) 0.2182 | grad 7.7073 | lr 0.0000 | time_forward 3.9140 | time_backward 5.3300 |
[2023-10-25 10:47:19,873::train::INFO] [train] Iter 594966 | loss 1.8645 | loss(rot) 1.8306 | loss(pos) 0.0337 | loss(seq) 0.0003 | grad 3.8405 | lr 0.0000 | time_forward 3.7040 | time_backward 5.3350 |
[2023-10-25 10:47:29,077::train::INFO] [train] Iter 594967 | loss 0.3112 | loss(rot) 0.1727 | loss(pos) 0.0316 | loss(seq) 0.1069 | grad 3.0812 | lr 0.0000 | time_forward 3.8770 | time_backward 5.3240 |
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