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[2023-10-25 19:37:18,028::train::INFO] [train] Iter 599553 | loss 0.8172 | loss(rot) 0.5239 | loss(pos) 0.0395 | loss(seq) 0.2538 | grad 3.4623 | lr 0.0000 | time_forward 4.3960 | time_backward 6.9210 |
[2023-10-25 19:37:29,627::train::INFO] [train] Iter 599554 | loss 1.0561 | loss(rot) 0.5896 | loss(pos) 0.1088 | loss(seq) 0.3578 | grad 3.5060 | lr 0.0000 | time_forward 4.5550 | time_backward 7.0400 |
[2023-10-25 19:37:32,093::train::INFO] [train] Iter 599555 | loss 0.1801 | loss(rot) 0.0825 | loss(pos) 0.0599 | loss(seq) 0.0377 | grad 2.1823 | lr 0.0000 | time_forward 1.1060 | time_backward 1.3570 |
[2023-10-25 19:37:42,525::train::INFO] [train] Iter 599556 | loss 0.3535 | loss(rot) 0.1316 | loss(pos) 0.0913 | loss(seq) 0.1306 | grad 2.8487 | lr 0.0000 | time_forward 4.3950 | time_backward 6.0340 |
[2023-10-25 19:37:52,454::train::INFO] [train] Iter 599557 | loss 2.2181 | loss(rot) 1.6708 | loss(pos) 0.1302 | loss(seq) 0.4171 | grad 7.2074 | lr 0.0000 | time_forward 4.1350 | time_backward 5.7900 |
[2023-10-25 19:38:03,013::train::INFO] [train] Iter 599558 | loss 0.1595 | loss(rot) 0.1207 | loss(pos) 0.0211 | loss(seq) 0.0178 | grad 2.5473 | lr 0.0000 | time_forward 4.5310 | time_backward 6.0230 |
[2023-10-25 19:38:06,014::train::INFO] [train] Iter 599559 | loss 0.5703 | loss(rot) 0.2440 | loss(pos) 0.0502 | loss(seq) 0.2761 | grad 3.3702 | lr 0.0000 | time_forward 1.4800 | time_backward 1.5170 |
[2023-10-25 19:38:17,303::train::INFO] [train] Iter 599560 | loss 0.5625 | loss(rot) 0.5359 | loss(pos) 0.0266 | loss(seq) 0.0000 | grad 3.7258 | lr 0.0000 | time_forward 4.5680 | time_backward 6.7170 |
[2023-10-25 19:38:26,795::train::INFO] [train] Iter 599561 | loss 1.0376 | loss(rot) 1.0002 | loss(pos) 0.0349 | loss(seq) 0.0025 | grad 5.7668 | lr 0.0000 | time_forward 3.8990 | time_backward 5.5880 |
[2023-10-25 19:38:31,002::train::INFO] [train] Iter 599562 | loss 1.8697 | loss(rot) 1.7728 | loss(pos) 0.0938 | loss(seq) 0.0031 | grad 3.9842 | lr 0.0000 | time_forward 1.8850 | time_backward 2.3110 |
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