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[2023-10-24 14:41:28,380::train::INFO] [train] Iter 584268 | loss 1.1637 | loss(rot) 0.9475 | loss(pos) 0.0777 | loss(seq) 0.1385 | grad 10.5131 | lr 0.0000 | time_forward 2.7970 | time_backward 3.5770 |
[2023-10-24 14:41:36,088::train::INFO] [train] Iter 584269 | loss 0.5120 | loss(rot) 0.0642 | loss(pos) 0.0417 | loss(seq) 0.4061 | grad 3.2586 | lr 0.0000 | time_forward 3.3220 | time_backward 4.3830 |
[2023-10-24 14:41:43,779::train::INFO] [train] Iter 584270 | loss 1.2222 | loss(rot) 0.7201 | loss(pos) 0.2740 | loss(seq) 0.2280 | grad 4.8864 | lr 0.0000 | time_forward 3.3110 | time_backward 4.3780 |
[2023-10-24 14:41:53,636::train::INFO] [train] Iter 584271 | loss 1.1517 | loss(rot) 0.9855 | loss(pos) 0.0326 | loss(seq) 0.1336 | grad 3.4037 | lr 0.0000 | time_forward 4.2540 | time_backward 5.6000 |
[2023-10-24 14:42:03,405::train::INFO] [train] Iter 584272 | loss 0.6009 | loss(rot) 0.5611 | loss(pos) 0.0310 | loss(seq) 0.0088 | grad 2.4152 | lr 0.0000 | time_forward 4.3750 | time_backward 5.3900 |
[2023-10-24 14:42:12,154::train::INFO] [train] Iter 584273 | loss 2.2687 | loss(rot) 1.5531 | loss(pos) 0.2524 | loss(seq) 0.4632 | grad 4.7553 | lr 0.0000 | time_forward 3.5520 | time_backward 5.1940 |
[2023-10-24 14:42:20,245::train::INFO] [train] Iter 584274 | loss 0.3331 | loss(rot) 0.0726 | loss(pos) 0.0245 | loss(seq) 0.2360 | grad 2.1870 | lr 0.0000 | time_forward 3.4700 | time_backward 4.6180 |
[2023-10-24 14:42:29,092::train::INFO] [train] Iter 584275 | loss 2.3003 | loss(rot) 1.8884 | loss(pos) 0.1028 | loss(seq) 0.3092 | grad 3.1471 | lr 0.0000 | time_forward 3.6180 | time_backward 5.2260 |
[2023-10-24 14:42:37,985::train::INFO] [train] Iter 584276 | loss 1.1758 | loss(rot) 0.7394 | loss(pos) 0.0598 | loss(seq) 0.3767 | grad 4.4154 | lr 0.0000 | time_forward 3.5790 | time_backward 5.3120 |
[2023-10-24 14:42:40,201::train::INFO] [train] Iter 584277 | loss 0.7175 | loss(rot) 0.6555 | loss(pos) 0.0356 | loss(seq) 0.0264 | grad 16.2689 | lr 0.0000 | time_forward 1.0130 | time_backward 1.2000 |
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