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[2023-10-23 23:02:15,780::train::INFO] [train] Iter 576376 | loss 3.8869 | loss(rot) 0.0118 | loss(pos) 3.8750 | loss(seq) 0.0000 | grad 16.6569 | lr 0.0000 | time_forward 5.4860 | time_backward 4.6430 |
[2023-10-23 23:02:27,667::train::INFO] [train] Iter 576377 | loss 0.2806 | loss(rot) 0.1796 | loss(pos) 0.0152 | loss(seq) 0.0858 | grad 2.4445 | lr 0.0000 | time_forward 7.1100 | time_backward 4.7730 |
[2023-10-23 23:02:30,505::train::INFO] [train] Iter 576378 | loss 0.6539 | loss(rot) 0.3024 | loss(pos) 0.2844 | loss(seq) 0.0672 | grad 3.5700 | lr 0.0000 | time_forward 1.4450 | time_backward 1.3900 |
[2023-10-23 23:02:37,215::train::INFO] [train] Iter 576379 | loss 0.3510 | loss(rot) 0.1199 | loss(pos) 0.2148 | loss(seq) 0.0163 | grad 4.0669 | lr 0.0000 | time_forward 3.2550 | time_backward 3.4380 |
[2023-10-23 23:02:47,266::train::INFO] [train] Iter 576380 | loss 0.5298 | loss(rot) 0.2742 | loss(pos) 0.0307 | loss(seq) 0.2249 | grad 4.9329 | lr 0.0000 | time_forward 5.4450 | time_backward 4.6030 |
[2023-10-23 23:02:55,353::train::INFO] [train] Iter 576381 | loss 0.6024 | loss(rot) 0.0786 | loss(pos) 0.2438 | loss(seq) 0.2799 | grad 7.2389 | lr 0.0000 | time_forward 3.7750 | time_backward 4.3090 |
[2023-10-23 23:03:05,076::train::INFO] [train] Iter 576382 | loss 0.7025 | loss(rot) 0.4875 | loss(pos) 0.0286 | loss(seq) 0.1864 | grad 1.9529 | lr 0.0000 | time_forward 4.3990 | time_backward 5.3210 |
[2023-10-23 23:03:07,652::train::INFO] [train] Iter 576383 | loss 0.1430 | loss(rot) 0.0812 | loss(pos) 0.0524 | loss(seq) 0.0094 | grad 2.3540 | lr 0.0000 | time_forward 1.1870 | time_backward 1.3860 |
[2023-10-23 23:03:10,588::train::INFO] [train] Iter 576384 | loss 0.3222 | loss(rot) 0.2847 | loss(pos) 0.0188 | loss(seq) 0.0187 | grad 2.9870 | lr 0.0000 | time_forward 1.4950 | time_backward 1.4380 |
[2023-10-23 23:03:25,714::train::INFO] [train] Iter 576385 | loss 1.1848 | loss(rot) 0.9168 | loss(pos) 0.1007 | loss(seq) 0.1673 | grad 5.2590 | lr 0.0000 | time_forward 6.5250 | time_backward 8.5990 |
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