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[2023-10-25 10:34:11,650::train::INFO] [train] Iter 594858 | loss 1.0660 | loss(rot) 0.6188 | loss(pos) 0.1177 | loss(seq) 0.3295 | grad 5.9072 | lr 0.0000 | time_forward 2.9830 | time_backward 3.9960 |
[2023-10-25 10:34:21,532::train::INFO] [train] Iter 594859 | loss 1.4276 | loss(rot) 1.3937 | loss(pos) 0.0338 | loss(seq) 0.0000 | grad 11.3463 | lr 0.0000 | time_forward 4.0440 | time_backward 5.8360 |
[2023-10-25 10:34:24,285::train::INFO] [train] Iter 594860 | loss 0.5928 | loss(rot) 0.5613 | loss(pos) 0.0306 | loss(seq) 0.0009 | grad 5.2953 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4380 |
[2023-10-25 10:34:27,003::train::INFO] [train] Iter 594861 | loss 2.0100 | loss(rot) 1.3750 | loss(pos) 0.1119 | loss(seq) 0.5232 | grad 11.3781 | lr 0.0000 | time_forward 1.2400 | time_backward 1.4500 |
[2023-10-25 10:34:32,598::train::INFO] [train] Iter 594862 | loss 0.1944 | loss(rot) 0.1711 | loss(pos) 0.0186 | loss(seq) 0.0046 | grad 2.4194 | lr 0.0000 | time_forward 2.3790 | time_backward 3.2120 |
[2023-10-25 10:34:41,365::train::INFO] [train] Iter 594863 | loss 0.5556 | loss(rot) 0.5363 | loss(pos) 0.0185 | loss(seq) 0.0008 | grad 3.6114 | lr 0.0000 | time_forward 3.7310 | time_backward 5.0150 |
[2023-10-25 10:34:49,648::train::INFO] [train] Iter 594864 | loss 0.6820 | loss(rot) 0.0477 | loss(pos) 0.6303 | loss(seq) 0.0040 | grad 6.5570 | lr 0.0000 | time_forward 3.5010 | time_backward 4.7790 |
[2023-10-25 10:34:57,686::train::INFO] [train] Iter 594865 | loss 0.7085 | loss(rot) 0.6604 | loss(pos) 0.0246 | loss(seq) 0.0235 | grad 14.4740 | lr 0.0000 | time_forward 3.3810 | time_backward 4.6530 |
[2023-10-25 10:35:00,411::train::INFO] [train] Iter 594866 | loss 0.1951 | loss(rot) 0.1531 | loss(pos) 0.0420 | loss(seq) 0.0000 | grad 2.4726 | lr 0.0000 | time_forward 1.3250 | time_backward 1.3970 |
[2023-10-25 10:35:08,772::train::INFO] [train] Iter 594867 | loss 0.9911 | loss(rot) 0.0486 | loss(pos) 0.9195 | loss(seq) 0.0230 | grad 7.0152 | lr 0.0000 | time_forward 3.6270 | time_backward 4.6840 |
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