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[2023-10-24 07:03:11,924::train::INFO] [train] Iter 580472 | loss 0.1059 | loss(rot) 0.0347 | loss(pos) 0.0191 | loss(seq) 0.0521 | grad 1.5600 | lr 0.0000 | time_forward 3.6360 | time_backward 5.0560 |
[2023-10-24 07:03:21,606::train::INFO] [train] Iter 580473 | loss 0.6690 | loss(rot) 0.2237 | loss(pos) 0.1691 | loss(seq) 0.2761 | grad 2.7375 | lr 0.0000 | time_forward 4.1720 | time_backward 5.5070 |
[2023-10-24 07:03:29,591::train::INFO] [train] Iter 580474 | loss 1.4206 | loss(rot) 1.3810 | loss(pos) 0.0390 | loss(seq) 0.0006 | grad 3.8358 | lr 0.0000 | time_forward 3.4370 | time_backward 4.5450 |
[2023-10-24 07:03:38,886::train::INFO] [train] Iter 580475 | loss 0.9031 | loss(rot) 0.2866 | loss(pos) 0.6002 | loss(seq) 0.0163 | grad 8.1946 | lr 0.0000 | time_forward 3.8430 | time_backward 5.4490 |
[2023-10-24 07:03:41,841::train::INFO] [train] Iter 580476 | loss 0.6601 | loss(rot) 0.4772 | loss(pos) 0.0302 | loss(seq) 0.1528 | grad 2.9800 | lr 0.0000 | time_forward 1.4810 | time_backward 1.4700 |
[2023-10-24 07:03:51,411::train::INFO] [train] Iter 580477 | loss 1.8328 | loss(rot) 1.5892 | loss(pos) 0.1225 | loss(seq) 0.1210 | grad 6.2659 | lr 0.0000 | time_forward 3.9970 | time_backward 5.5710 |
[2023-10-24 07:03:59,978::train::INFO] [train] Iter 580478 | loss 0.7242 | loss(rot) 0.6823 | loss(pos) 0.0276 | loss(seq) 0.0142 | grad 4.7305 | lr 0.0000 | time_forward 3.6570 | time_backward 4.9070 |
[2023-10-24 07:04:03,107::train::INFO] [train] Iter 580479 | loss 0.2024 | loss(rot) 0.1023 | loss(pos) 0.0309 | loss(seq) 0.0693 | grad 3.0573 | lr 0.0000 | time_forward 1.5370 | time_backward 1.5890 |
[2023-10-24 07:04:11,790::train::INFO] [train] Iter 580480 | loss 0.7684 | loss(rot) 0.3466 | loss(pos) 0.3343 | loss(seq) 0.0875 | grad 5.5880 | lr 0.0000 | time_forward 3.7280 | time_backward 4.9520 |
[2023-10-24 07:04:21,177::train::INFO] [train] Iter 580481 | loss 0.3878 | loss(rot) 0.0231 | loss(pos) 0.3635 | loss(seq) 0.0012 | grad 9.4091 | lr 0.0000 | time_forward 3.8410 | time_backward 5.5430 |
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