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[2023-10-23 01:33:23,677::train::INFO] [train] Iter 564488 | loss 1.5389 | loss(rot) 1.1116 | loss(pos) 0.0764 | loss(seq) 0.3509 | grad 4.4087 | lr 0.0000 | time_forward 3.3390 | time_backward 4.6360 |
[2023-10-23 01:33:30,210::train::INFO] [train] Iter 564489 | loss 0.3628 | loss(rot) 0.3065 | loss(pos) 0.0547 | loss(seq) 0.0015 | grad 2.4447 | lr 0.0000 | time_forward 2.7930 | time_backward 3.7360 |
[2023-10-23 01:33:38,091::train::INFO] [train] Iter 564490 | loss 0.8579 | loss(rot) 0.2284 | loss(pos) 0.2372 | loss(seq) 0.3923 | grad 3.3813 | lr 0.0000 | time_forward 3.2630 | time_backward 4.6150 |
[2023-10-23 01:33:45,095::train::INFO] [train] Iter 564491 | loss 1.3351 | loss(rot) 1.1235 | loss(pos) 0.0767 | loss(seq) 0.1349 | grad 3.3853 | lr 0.0000 | time_forward 3.0070 | time_backward 3.9940 |
[2023-10-23 01:33:53,071::train::INFO] [train] Iter 564492 | loss 2.2204 | loss(rot) 1.9876 | loss(pos) 0.0606 | loss(seq) 0.1721 | grad 4.4328 | lr 0.0000 | time_forward 3.3090 | time_backward 4.6630 |
[2023-10-23 01:34:00,121::train::INFO] [train] Iter 564493 | loss 0.2795 | loss(rot) 0.0843 | loss(pos) 0.0434 | loss(seq) 0.1518 | grad 2.8159 | lr 0.0000 | time_forward 3.0310 | time_backward 4.0150 |
[2023-10-23 01:34:06,318::train::INFO] [train] Iter 564494 | loss 0.7704 | loss(rot) 0.2977 | loss(pos) 0.0352 | loss(seq) 0.4375 | grad 4.6172 | lr 0.0000 | time_forward 2.6690 | time_backward 3.5240 |
[2023-10-23 01:34:09,040::train::INFO] [train] Iter 564495 | loss 0.4163 | loss(rot) 0.1315 | loss(pos) 0.0359 | loss(seq) 0.2490 | grad 2.3137 | lr 0.0000 | time_forward 1.2820 | time_backward 1.4360 |
[2023-10-23 01:34:17,128::train::INFO] [train] Iter 564496 | loss 0.3153 | loss(rot) 0.2287 | loss(pos) 0.0230 | loss(seq) 0.0636 | grad 2.2440 | lr 0.0000 | time_forward 3.4080 | time_backward 4.6770 |
[2023-10-23 01:34:24,492::train::INFO] [train] Iter 564497 | loss 0.1333 | loss(rot) 0.1035 | loss(pos) 0.0292 | loss(seq) 0.0006 | grad 2.1347 | lr 0.0000 | time_forward 3.2400 | time_backward 4.1210 |
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