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[2023-09-02 00:52:59,119::train::INFO] [train] Iter 04181 | loss 1.0498 | loss(rot) 0.4081 | loss(pos) 0.2983 | loss(seq) 0.3435 | grad 3.5669 | lr 0.0010 | time_forward 3.9230 | time_backward 5.6270 |
[2023-09-02 00:53:07,057::train::INFO] [train] Iter 04182 | loss 1.8637 | loss(rot) 0.0981 | loss(pos) 1.7593 | loss(seq) 0.0063 | grad 5.8502 | lr 0.0010 | time_forward 3.3640 | time_backward 4.5710 |
[2023-09-02 00:53:14,609::train::INFO] [train] Iter 04183 | loss 1.3347 | loss(rot) 0.1801 | loss(pos) 1.1068 | loss(seq) 0.0478 | grad 3.3366 | lr 0.0010 | time_forward 3.2260 | time_backward 4.3210 |
[2023-09-02 00:53:23,346::train::INFO] [train] Iter 04184 | loss 3.1452 | loss(rot) 2.9824 | loss(pos) 0.1595 | loss(seq) 0.0034 | grad 2.3710 | lr 0.0010 | time_forward 3.6740 | time_backward 5.0600 |
[2023-09-02 00:53:30,561::train::INFO] [train] Iter 04185 | loss 2.7713 | loss(rot) 2.3901 | loss(pos) 0.1173 | loss(seq) 0.2640 | grad 4.9117 | lr 0.0010 | time_forward 3.0090 | time_backward 4.2020 |
[2023-09-02 00:53:39,132::train::INFO] [train] Iter 04186 | loss 1.6505 | loss(rot) 1.0202 | loss(pos) 0.1443 | loss(seq) 0.4859 | grad 4.2630 | lr 0.0010 | time_forward 3.6120 | time_backward 4.9570 |
[2023-09-02 00:53:49,381::train::INFO] [train] Iter 04187 | loss 0.9009 | loss(rot) 0.2612 | loss(pos) 0.5997 | loss(seq) 0.0400 | grad 4.4187 | lr 0.0010 | time_forward 4.1710 | time_backward 6.0740 |
[2023-09-02 00:53:59,492::train::INFO] [train] Iter 04188 | loss 1.0106 | loss(rot) 0.5086 | loss(pos) 0.3409 | loss(seq) 0.1611 | grad 4.3996 | lr 0.0010 | time_forward 4.0720 | time_backward 6.0350 |
[2023-09-02 00:54:12,093::train::INFO] [train] Iter 04189 | loss 3.0884 | loss(rot) 1.8937 | loss(pos) 0.5657 | loss(seq) 0.6291 | grad 5.4963 | lr 0.0010 | time_forward 4.4880 | time_backward 8.1100 |
[2023-09-02 00:54:19,822::train::INFO] [train] Iter 04190 | loss 1.2504 | loss(rot) 0.7893 | loss(pos) 0.3087 | loss(seq) 0.1524 | grad 4.1608 | lr 0.0010 | time_forward 3.2210 | time_backward 4.5050 |
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