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[2023-10-25 05:41:53,565::train::INFO] [train] Iter 592260 | loss 0.4328 | loss(rot) 0.2180 | loss(pos) 0.0307 | loss(seq) 0.1841 | grad 2.8475 | lr 0.0000 | time_forward 3.6690 | time_backward 5.3020 |
[2023-10-25 05:42:02,524::train::INFO] [train] Iter 592261 | loss 0.2870 | loss(rot) 0.0407 | loss(pos) 0.2338 | loss(seq) 0.0125 | grad 3.4007 | lr 0.0000 | time_forward 3.6400 | time_backward 5.3160 |
[2023-10-25 05:42:05,329::train::INFO] [train] Iter 592262 | loss 0.5864 | loss(rot) 0.5383 | loss(pos) 0.0272 | loss(seq) 0.0209 | grad 5.2637 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4940 |
[2023-10-25 05:42:08,038::train::INFO] [train] Iter 592263 | loss 0.3142 | loss(rot) 0.0984 | loss(pos) 0.0320 | loss(seq) 0.1839 | grad 2.3460 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4130 |
[2023-10-25 05:42:16,220::train::INFO] [train] Iter 592264 | loss 0.2304 | loss(rot) 0.1182 | loss(pos) 0.0218 | loss(seq) 0.0904 | grad 2.2590 | lr 0.0000 | time_forward 3.4920 | time_backward 4.6880 |
[2023-10-25 05:42:18,457::train::INFO] [train] Iter 592265 | loss 0.8317 | loss(rot) 0.8014 | loss(pos) 0.0199 | loss(seq) 0.0104 | grad 15.1260 | lr 0.0000 | time_forward 1.0280 | time_backward 1.2050 |
[2023-10-25 05:42:27,405::train::INFO] [train] Iter 592266 | loss 0.2387 | loss(rot) 0.0356 | loss(pos) 0.0148 | loss(seq) 0.1883 | grad 1.8804 | lr 0.0000 | time_forward 3.6100 | time_backward 5.3360 |
[2023-10-25 05:42:30,243::train::INFO] [train] Iter 592267 | loss 1.5904 | loss(rot) 0.0084 | loss(pos) 1.5815 | loss(seq) 0.0004 | grad 12.1786 | lr 0.0000 | time_forward 1.2690 | time_backward 1.5650 |
[2023-10-25 05:42:39,305::train::INFO] [train] Iter 592268 | loss 0.8289 | loss(rot) 0.6876 | loss(pos) 0.0279 | loss(seq) 0.1133 | grad 2.7905 | lr 0.0000 | time_forward 3.8370 | time_backward 5.2210 |
[2023-10-25 05:42:48,233::train::INFO] [train] Iter 592269 | loss 0.5275 | loss(rot) 0.2794 | loss(pos) 0.0225 | loss(seq) 0.2256 | grad 2.6735 | lr 0.0000 | time_forward 3.6380 | time_backward 5.2870 |
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