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[2023-10-25 14:36:35,931::train::INFO] [train] Iter 596856 | loss 0.4138 | loss(rot) 0.0603 | loss(pos) 0.1092 | loss(seq) 0.2443 | grad 3.1870 | lr 0.0000 | time_forward 2.9450 | time_backward 3.7700 |
[2023-10-25 14:36:38,228::train::INFO] [train] Iter 596857 | loss 1.1133 | loss(rot) 0.4985 | loss(pos) 0.2282 | loss(seq) 0.3866 | grad 4.4046 | lr 0.0000 | time_forward 1.0430 | time_backward 1.2510 |
[2023-10-25 14:36:47,776::train::INFO] [train] Iter 596858 | loss 0.2082 | loss(rot) 0.1859 | loss(pos) 0.0222 | loss(seq) 0.0000 | grad 2.6265 | lr 0.0000 | time_forward 3.8290 | time_backward 5.7160 |
[2023-10-25 14:36:56,770::train::INFO] [train] Iter 596859 | loss 0.9794 | loss(rot) 0.6521 | loss(pos) 0.1761 | loss(seq) 0.1512 | grad 4.5489 | lr 0.0000 | time_forward 3.8550 | time_backward 5.1360 |
[2023-10-25 14:36:59,693::train::INFO] [train] Iter 596860 | loss 0.7129 | loss(rot) 0.4672 | loss(pos) 0.1681 | loss(seq) 0.0777 | grad 4.1849 | lr 0.0000 | time_forward 1.4560 | time_backward 1.4640 |
[2023-10-25 14:37:02,662::train::INFO] [train] Iter 596861 | loss 0.7540 | loss(rot) 0.3329 | loss(pos) 0.1214 | loss(seq) 0.2997 | grad 3.3622 | lr 0.0000 | time_forward 1.4850 | time_backward 1.4810 |
[2023-10-25 14:37:12,736::train::INFO] [train] Iter 596862 | loss 1.1640 | loss(rot) 1.1254 | loss(pos) 0.0171 | loss(seq) 0.0215 | grad 5.5895 | lr 0.0000 | time_forward 4.3880 | time_backward 5.6820 |
[2023-10-25 14:37:20,928::train::INFO] [train] Iter 596863 | loss 1.2347 | loss(rot) 0.9753 | loss(pos) 0.0268 | loss(seq) 0.2325 | grad 5.3363 | lr 0.0000 | time_forward 3.4910 | time_backward 4.6990 |
[2023-10-25 14:37:29,098::train::INFO] [train] Iter 596864 | loss 0.8524 | loss(rot) 0.2172 | loss(pos) 0.6287 | loss(seq) 0.0065 | grad 6.1839 | lr 0.0000 | time_forward 3.4590 | time_backward 4.7070 |
[2023-10-25 14:37:31,772::train::INFO] [train] Iter 596865 | loss 0.6599 | loss(rot) 0.2329 | loss(pos) 0.0800 | loss(seq) 0.3470 | grad 3.3848 | lr 0.0000 | time_forward 1.2220 | time_backward 1.4500 |
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