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[2023-10-22 15:55:03,729::train::INFO] [train] Iter 559294 | loss 0.4837 | loss(rot) 0.4507 | loss(pos) 0.0174 | loss(seq) 0.0156 | grad 1.9835 | lr 0.0000 | time_forward 3.9090 | time_backward 5.6560 |
[2023-10-22 15:55:11,976::train::INFO] [train] Iter 559295 | loss 0.5138 | loss(rot) 0.1167 | loss(pos) 0.0654 | loss(seq) 0.3317 | grad 2.9679 | lr 0.0000 | time_forward 3.4380 | time_backward 4.8060 |
[2023-10-22 15:55:20,406::train::INFO] [train] Iter 559296 | loss 0.1785 | loss(rot) 0.0367 | loss(pos) 0.0560 | loss(seq) 0.0857 | grad 2.3304 | lr 0.0000 | time_forward 3.5420 | time_backward 4.8840 |
[2023-10-22 15:55:29,792::train::INFO] [train] Iter 559297 | loss 1.4257 | loss(rot) 1.0991 | loss(pos) 0.0421 | loss(seq) 0.2845 | grad 2.7683 | lr 0.0000 | time_forward 3.9430 | time_backward 5.4390 |
[2023-10-22 15:55:37,689::train::INFO] [train] Iter 559298 | loss 1.9245 | loss(rot) 0.6862 | loss(pos) 0.5709 | loss(seq) 0.6674 | grad 7.1291 | lr 0.0000 | time_forward 3.3090 | time_backward 4.5850 |
[2023-10-22 15:55:45,488::train::INFO] [train] Iter 559299 | loss 1.0937 | loss(rot) 0.9822 | loss(pos) 0.0301 | loss(seq) 0.0814 | grad 52.3390 | lr 0.0000 | time_forward 3.2410 | time_backward 4.5540 |
[2023-10-22 15:55:53,382::train::INFO] [train] Iter 559300 | loss 0.1137 | loss(rot) 0.0862 | loss(pos) 0.0153 | loss(seq) 0.0122 | grad 2.1796 | lr 0.0000 | time_forward 3.2950 | time_backward 4.5960 |
[2023-10-22 15:56:02,309::train::INFO] [train] Iter 559301 | loss 0.5324 | loss(rot) 0.2783 | loss(pos) 0.0547 | loss(seq) 0.1993 | grad 5.2155 | lr 0.0000 | time_forward 3.7700 | time_backward 5.1530 |
[2023-10-22 15:56:11,915::train::INFO] [train] Iter 559302 | loss 0.8654 | loss(rot) 0.8331 | loss(pos) 0.0155 | loss(seq) 0.0168 | grad 20.0982 | lr 0.0000 | time_forward 3.8920 | time_backward 5.7110 |
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