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[2023-10-24 10:43:39,683::train::INFO] [train] Iter 582270 | loss 0.2220 | loss(rot) 0.0401 | loss(pos) 0.1795 | loss(seq) 0.0023 | grad 2.7355 | lr 0.0000 | time_forward 4.1940 | time_backward 6.1000 |
[2023-10-24 10:43:49,149::train::INFO] [train] Iter 582271 | loss 0.5873 | loss(rot) 0.0878 | loss(pos) 0.4863 | loss(seq) 0.0132 | grad 9.6248 | lr 0.0000 | time_forward 4.0850 | time_backward 5.3780 |
[2023-10-24 10:43:58,882::train::INFO] [train] Iter 582272 | loss 1.0557 | loss(rot) 1.0157 | loss(pos) 0.0325 | loss(seq) 0.0075 | grad 11.3837 | lr 0.0000 | time_forward 3.9410 | time_backward 5.7890 |
[2023-10-24 10:44:08,854::train::INFO] [train] Iter 582273 | loss 1.0187 | loss(rot) 0.6251 | loss(pos) 0.0503 | loss(seq) 0.3433 | grad 13.1467 | lr 0.0000 | time_forward 4.1530 | time_backward 5.8160 |
[2023-10-24 10:44:11,676::train::INFO] [train] Iter 582274 | loss 0.4324 | loss(rot) 0.4023 | loss(pos) 0.0290 | loss(seq) 0.0011 | grad 14.4174 | lr 0.0000 | time_forward 1.3740 | time_backward 1.4440 |
[2023-10-24 10:44:21,024::train::INFO] [train] Iter 582275 | loss 0.6193 | loss(rot) 0.1884 | loss(pos) 0.2112 | loss(seq) 0.2196 | grad 4.5749 | lr 0.0000 | time_forward 3.9050 | time_backward 5.4050 |
[2023-10-24 10:44:23,752::train::INFO] [train] Iter 582276 | loss 2.1906 | loss(rot) 1.7547 | loss(pos) 0.2122 | loss(seq) 0.2238 | grad 8.2711 | lr 0.0000 | time_forward 1.3170 | time_backward 1.4080 |
[2023-10-24 10:44:33,785::train::INFO] [train] Iter 582277 | loss 0.2740 | loss(rot) 0.1672 | loss(pos) 0.0209 | loss(seq) 0.0860 | grad 2.4821 | lr 0.0000 | time_forward 4.1200 | time_backward 5.9100 |
[2023-10-24 10:44:42,563::train::INFO] [train] Iter 582278 | loss 0.5356 | loss(rot) 0.0932 | loss(pos) 0.0197 | loss(seq) 0.4227 | grad 2.2129 | lr 0.0000 | time_forward 3.7190 | time_backward 5.0560 |
[2023-10-24 10:44:45,310::train::INFO] [train] Iter 582279 | loss 0.5775 | loss(rot) 0.4484 | loss(pos) 0.0231 | loss(seq) 0.1060 | grad 6.3454 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4350 |
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