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[2023-10-24 04:31:35,794::train::INFO] [train] Iter 579173 | loss 0.8048 | loss(rot) 0.3936 | loss(pos) 0.1271 | loss(seq) 0.2841 | grad 3.4469 | lr 0.0000 | time_forward 3.9100 | time_backward 5.6240 |
[2023-10-24 04:31:38,324::train::INFO] [train] Iter 579174 | loss 0.4413 | loss(rot) 0.1034 | loss(pos) 0.0560 | loss(seq) 0.2818 | grad 2.8201 | lr 0.0000 | time_forward 1.1900 | time_backward 1.3360 |
[2023-10-24 04:31:41,210::train::INFO] [train] Iter 579175 | loss 2.0877 | loss(rot) 0.0061 | loss(pos) 2.0814 | loss(seq) 0.0002 | grad 35.2930 | lr 0.0000 | time_forward 1.3430 | time_backward 1.5400 |
[2023-10-24 04:31:49,216::train::INFO] [train] Iter 579176 | loss 1.3965 | loss(rot) 0.0213 | loss(pos) 1.3725 | loss(seq) 0.0027 | grad 9.3139 | lr 0.0000 | time_forward 3.2790 | time_backward 4.7250 |
[2023-10-24 04:31:59,120::train::INFO] [train] Iter 579177 | loss 0.1405 | loss(rot) 0.0965 | loss(pos) 0.0281 | loss(seq) 0.0159 | grad 1.4811 | lr 0.0000 | time_forward 4.1560 | time_backward 5.7440 |
[2023-10-24 04:32:07,286::train::INFO] [train] Iter 579178 | loss 0.5376 | loss(rot) 0.3029 | loss(pos) 0.1325 | loss(seq) 0.1023 | grad 5.2872 | lr 0.0000 | time_forward 3.4860 | time_backward 4.6780 |
[2023-10-24 04:32:15,485::train::INFO] [train] Iter 579179 | loss 1.7286 | loss(rot) 1.7141 | loss(pos) 0.0123 | loss(seq) 0.0022 | grad 5.0847 | lr 0.0000 | time_forward 3.5080 | time_backward 4.6870 |
[2023-10-24 04:32:23,604::train::INFO] [train] Iter 579180 | loss 0.7250 | loss(rot) 0.2334 | loss(pos) 0.0468 | loss(seq) 0.4448 | grad 4.0134 | lr 0.0000 | time_forward 3.4280 | time_backward 4.6880 |
[2023-10-24 04:32:31,810::train::INFO] [train] Iter 579181 | loss 1.0091 | loss(rot) 0.2306 | loss(pos) 0.2464 | loss(seq) 0.5321 | grad 5.1147 | lr 0.0000 | time_forward 3.4240 | time_backward 4.7780 |
[2023-10-24 04:32:41,454::train::INFO] [train] Iter 579182 | loss 0.5033 | loss(rot) 0.0661 | loss(pos) 0.4224 | loss(seq) 0.0148 | grad 5.5183 | lr 0.0000 | time_forward 3.8890 | time_backward 5.7520 |
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