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[2023-10-25 10:58:51,212::train::INFO] [train] Iter 595057 | loss 1.8009 | loss(rot) 1.7049 | loss(pos) 0.0314 | loss(seq) 0.0646 | grad 4.5072 | lr 0.0000 | time_forward 3.5800 | time_backward 4.9170 |
[2023-10-25 10:58:53,961::train::INFO] [train] Iter 595058 | loss 0.7220 | loss(rot) 0.5470 | loss(pos) 0.0283 | loss(seq) 0.1467 | grad 2.3077 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4320 |
[2023-10-25 10:58:57,433::train::INFO] [train] Iter 595059 | loss 0.8328 | loss(rot) 0.1894 | loss(pos) 0.6106 | loss(seq) 0.0327 | grad 4.6796 | lr 0.0000 | time_forward 1.5930 | time_backward 1.8760 |
[2023-10-25 10:59:00,715::train::INFO] [train] Iter 595060 | loss 0.8538 | loss(rot) 0.8033 | loss(pos) 0.0186 | loss(seq) 0.0319 | grad 55.2561 | lr 0.0000 | time_forward 1.3970 | time_backward 1.8820 |
[2023-10-25 10:59:10,651::train::INFO] [train] Iter 595061 | loss 0.6219 | loss(rot) 0.4326 | loss(pos) 0.0741 | loss(seq) 0.1152 | grad 6.2340 | lr 0.0000 | time_forward 4.0800 | time_backward 5.8410 |
[2023-10-25 10:59:13,429::train::INFO] [train] Iter 595062 | loss 0.2701 | loss(rot) 0.0430 | loss(pos) 0.2077 | loss(seq) 0.0193 | grad 2.7352 | lr 0.0000 | time_forward 1.2590 | time_backward 1.5160 |
[2023-10-25 10:59:23,328::train::INFO] [train] Iter 595063 | loss 0.5711 | loss(rot) 0.5075 | loss(pos) 0.0184 | loss(seq) 0.0452 | grad 39.8264 | lr 0.0000 | time_forward 4.0350 | time_backward 5.8350 |
[2023-10-25 10:59:32,428::train::INFO] [train] Iter 595064 | loss 0.3785 | loss(rot) 0.1705 | loss(pos) 0.0217 | loss(seq) 0.1864 | grad 3.0979 | lr 0.0000 | time_forward 3.8680 | time_backward 5.2290 |
[2023-10-25 10:59:41,143::train::INFO] [train] Iter 595065 | loss 0.5253 | loss(rot) 0.1444 | loss(pos) 0.0295 | loss(seq) 0.3514 | grad 2.9075 | lr 0.0000 | time_forward 3.6930 | time_backward 5.0180 |
[2023-10-25 10:59:43,888::train::INFO] [train] Iter 595066 | loss 0.6213 | loss(rot) 0.5803 | loss(pos) 0.0410 | loss(seq) 0.0000 | grad 13.9116 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4240 |
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