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[2023-09-02 04:19:31,517::train::INFO] [train] Iter 05880 | loss 1.9567 | loss(rot) 1.6162 | loss(pos) 0.2433 | loss(seq) 0.0972 | grad 5.8438 | lr 0.0010 | time_forward 3.4790 | time_backward 4.9430 |
[2023-09-02 04:19:34,148::train::INFO] [train] Iter 05881 | loss 1.7973 | loss(rot) 1.4505 | loss(pos) 0.1440 | loss(seq) 0.2028 | grad 4.5284 | lr 0.0010 | time_forward 1.2490 | time_backward 1.3800 |
[2023-09-02 04:19:36,874::train::INFO] [train] Iter 05882 | loss 0.8110 | loss(rot) 0.0423 | loss(pos) 0.7526 | loss(seq) 0.0162 | grad 4.1719 | lr 0.0010 | time_forward 1.2850 | time_backward 1.4370 |
[2023-09-02 04:19:39,484::train::INFO] [train] Iter 05883 | loss 2.2671 | loss(rot) 1.7382 | loss(pos) 0.2038 | loss(seq) 0.3251 | grad 2.9875 | lr 0.0010 | time_forward 1.2330 | time_backward 1.3740 |
[2023-09-02 04:19:49,706::train::INFO] [train] Iter 05884 | loss 1.6444 | loss(rot) 0.7882 | loss(pos) 0.2064 | loss(seq) 0.6498 | grad 4.2298 | lr 0.0010 | time_forward 4.2540 | time_backward 5.9650 |
[2023-09-02 04:19:58,479::train::INFO] [train] Iter 05885 | loss 1.2338 | loss(rot) 0.1255 | loss(pos) 0.8932 | loss(seq) 0.2151 | grad 5.0149 | lr 0.0010 | time_forward 3.6740 | time_backward 5.0950 |
[2023-09-02 04:20:01,156::train::INFO] [train] Iter 05886 | loss 0.8482 | loss(rot) 0.2774 | loss(pos) 0.5177 | loss(seq) 0.0531 | grad 4.8983 | lr 0.0010 | time_forward 1.2710 | time_backward 1.4020 |
[2023-09-02 04:20:03,884::train::INFO] [train] Iter 05887 | loss 1.8726 | loss(rot) 1.4011 | loss(pos) 0.1830 | loss(seq) 0.2886 | grad 4.5191 | lr 0.0010 | time_forward 1.2980 | time_backward 1.4280 |
[2023-09-02 04:20:14,012::train::INFO] [train] Iter 05888 | loss 2.1816 | loss(rot) 2.0625 | loss(pos) 0.1159 | loss(seq) 0.0031 | grad 3.4892 | lr 0.0010 | time_forward 4.1160 | time_backward 6.0060 |
[2023-09-02 04:20:22,272::train::INFO] [train] Iter 05889 | loss 3.1535 | loss(rot) 0.0261 | loss(pos) 3.1270 | loss(seq) 0.0005 | grad 8.0666 | lr 0.0010 | time_forward 3.4790 | time_backward 4.7780 |
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