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[2023-10-23 00:43:03,142::train::INFO] [train] Iter 563989 | loss 0.4309 | loss(rot) 0.1182 | loss(pos) 0.0228 | loss(seq) 0.2899 | grad 2.8239 | lr 0.0000 | time_forward 3.1510 | time_backward 4.0490 |
[2023-10-23 00:43:05,341::train::INFO] [train] Iter 563990 | loss 1.0895 | loss(rot) 0.7091 | loss(pos) 0.0981 | loss(seq) 0.2822 | grad 3.7598 | lr 0.0000 | time_forward 1.0130 | time_backward 1.1820 |
[2023-10-23 00:43:12,041::train::INFO] [train] Iter 563991 | loss 0.4841 | loss(rot) 0.1812 | loss(pos) 0.1063 | loss(seq) 0.1966 | grad 4.3542 | lr 0.0000 | time_forward 2.9020 | time_backward 3.7890 |
[2023-10-23 00:43:18,662::train::INFO] [train] Iter 563992 | loss 0.2254 | loss(rot) 0.0295 | loss(pos) 0.1918 | loss(seq) 0.0041 | grad 4.1323 | lr 0.0000 | time_forward 2.8810 | time_backward 3.7380 |
[2023-10-23 00:43:21,741::train::INFO] [train] Iter 563993 | loss 0.3729 | loss(rot) 0.0327 | loss(pos) 0.3328 | loss(seq) 0.0074 | grad 4.6165 | lr 0.0000 | time_forward 1.4050 | time_backward 1.6700 |
[2023-10-23 00:43:28,305::train::INFO] [train] Iter 563994 | loss 0.2399 | loss(rot) 0.2070 | loss(pos) 0.0215 | loss(seq) 0.0114 | grad 2.5559 | lr 0.0000 | time_forward 2.8720 | time_backward 3.6800 |
[2023-10-23 00:43:30,995::train::INFO] [train] Iter 563995 | loss 0.9338 | loss(rot) 0.8798 | loss(pos) 0.0284 | loss(seq) 0.0256 | grad 4.6082 | lr 0.0000 | time_forward 1.2530 | time_backward 1.4330 |
[2023-10-23 00:43:38,928::train::INFO] [train] Iter 563996 | loss 0.7240 | loss(rot) 0.6530 | loss(pos) 0.0358 | loss(seq) 0.0352 | grad 3.8431 | lr 0.0000 | time_forward 3.4540 | time_backward 4.4760 |
[2023-10-23 00:43:45,869::train::INFO] [train] Iter 563997 | loss 0.2234 | loss(rot) 0.1888 | loss(pos) 0.0150 | loss(seq) 0.0196 | grad 1.8846 | lr 0.0000 | time_forward 2.9790 | time_backward 3.9590 |
[2023-10-23 00:43:53,740::train::INFO] [train] Iter 563998 | loss 1.3254 | loss(rot) 0.3690 | loss(pos) 0.6325 | loss(seq) 0.3239 | grad 5.5524 | lr 0.0000 | time_forward 3.2590 | time_backward 4.6090 |
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