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[2023-10-24 00:45:47,563::train::INFO] [train] Iter 577276 | loss 1.2373 | loss(rot) 1.1876 | loss(pos) 0.0289 | loss(seq) 0.0208 | grad 4.4064 | lr 0.0000 | time_forward 4.1240 | time_backward 6.0680 |
[2023-10-24 00:45:57,004::train::INFO] [train] Iter 577277 | loss 0.3837 | loss(rot) 0.0142 | loss(pos) 0.3675 | loss(seq) 0.0020 | grad 7.2589 | lr 0.0000 | time_forward 3.9840 | time_backward 5.4540 |
[2023-10-24 00:46:05,980::train::INFO] [train] Iter 577278 | loss 0.1933 | loss(rot) 0.0690 | loss(pos) 0.0198 | loss(seq) 0.1045 | grad 2.4167 | lr 0.0000 | time_forward 3.8830 | time_backward 5.0910 |
[2023-10-24 00:46:15,047::train::INFO] [train] Iter 577279 | loss 0.7929 | loss(rot) 0.5435 | loss(pos) 0.0886 | loss(seq) 0.1608 | grad 3.8063 | lr 0.0000 | time_forward 3.7820 | time_backward 5.2810 |
[2023-10-24 00:46:25,197::train::INFO] [train] Iter 577280 | loss 0.4879 | loss(rot) 0.1184 | loss(pos) 0.3615 | loss(seq) 0.0080 | grad 5.3783 | lr 0.0000 | time_forward 4.1750 | time_backward 5.9730 |
[2023-10-24 00:46:35,605::train::INFO] [train] Iter 577281 | loss 0.2836 | loss(rot) 0.2349 | loss(pos) 0.0485 | loss(seq) 0.0002 | grad 2.6293 | lr 0.0000 | time_forward 4.3950 | time_backward 6.0080 |
[2023-10-24 00:46:46,060::train::INFO] [train] Iter 577282 | loss 0.4682 | loss(rot) 0.1881 | loss(pos) 0.1387 | loss(seq) 0.1414 | grad 3.2033 | lr 0.0000 | time_forward 4.5440 | time_backward 5.9080 |
[2023-10-24 00:46:48,835::train::INFO] [train] Iter 577283 | loss 0.3094 | loss(rot) 0.1126 | loss(pos) 0.1644 | loss(seq) 0.0323 | grad 2.8437 | lr 0.0000 | time_forward 1.4210 | time_backward 1.3500 |
[2023-10-24 00:46:59,530::train::INFO] [train] Iter 577284 | loss 0.4255 | loss(rot) 0.4007 | loss(pos) 0.0217 | loss(seq) 0.0031 | grad 4.4411 | lr 0.0000 | time_forward 4.4390 | time_backward 6.2530 |
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