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[2023-10-23 04:32:40,157::train::INFO] [train] Iter 566286 | loss 1.7488 | loss(rot) 1.1012 | loss(pos) 0.2154 | loss(seq) 0.4322 | grad 5.6239 | lr 0.0000 | time_forward 2.8340 | time_backward 3.7370 |
[2023-10-23 04:32:48,113::train::INFO] [train] Iter 566287 | loss 1.5966 | loss(rot) 1.0885 | loss(pos) 0.0825 | loss(seq) 0.4256 | grad 4.9584 | lr 0.0000 | time_forward 3.2960 | time_backward 4.6560 |
[2023-10-23 04:32:51,198::train::INFO] [train] Iter 566288 | loss 0.6805 | loss(rot) 0.0492 | loss(pos) 0.6252 | loss(seq) 0.0061 | grad 6.9204 | lr 0.0000 | time_forward 1.3970 | time_backward 1.6850 |
[2023-10-23 04:32:53,404::train::INFO] [train] Iter 566289 | loss 0.7488 | loss(rot) 0.1036 | loss(pos) 0.1580 | loss(seq) 0.4872 | grad 3.2966 | lr 0.0000 | time_forward 1.0070 | time_backward 1.1970 |
[2023-10-23 04:33:00,530::train::INFO] [train] Iter 566290 | loss 0.2669 | loss(rot) 0.0706 | loss(pos) 0.0238 | loss(seq) 0.1726 | grad 1.9461 | lr 0.0000 | time_forward 3.0910 | time_backward 4.0310 |
[2023-10-23 04:33:08,043::train::INFO] [train] Iter 566291 | loss 0.4189 | loss(rot) 0.1025 | loss(pos) 0.0671 | loss(seq) 0.2494 | grad 3.1072 | lr 0.0000 | time_forward 3.3850 | time_backward 4.1260 |
[2023-10-23 04:33:14,251::train::INFO] [train] Iter 566292 | loss 0.1542 | loss(rot) 0.0912 | loss(pos) 0.0395 | loss(seq) 0.0236 | grad 2.6055 | lr 0.0000 | time_forward 2.6910 | time_backward 3.5140 |
[2023-10-23 04:33:22,141::train::INFO] [train] Iter 566293 | loss 0.4417 | loss(rot) 0.1987 | loss(pos) 0.0492 | loss(seq) 0.1937 | grad 2.1448 | lr 0.0000 | time_forward 3.2710 | time_backward 4.6160 |
[2023-10-23 04:33:24,950::train::INFO] [train] Iter 566294 | loss 0.9394 | loss(rot) 0.6243 | loss(pos) 0.0436 | loss(seq) 0.2715 | grad 6.1535 | lr 0.0000 | time_forward 1.2560 | time_backward 1.5500 |
[2023-10-23 04:33:32,888::train::INFO] [train] Iter 566295 | loss 1.2845 | loss(rot) 0.7564 | loss(pos) 0.2147 | loss(seq) 0.3134 | grad 3.8028 | lr 0.0000 | time_forward 3.2830 | time_backward 4.6380 |
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