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[2023-10-22 14:41:14,104::train::INFO] [train] Iter 558694 | loss 0.5836 | loss(rot) 0.2516 | loss(pos) 0.1248 | loss(seq) 0.2072 | grad 2.4600 | lr 0.0000 | time_forward 4.1990 | time_backward 5.7000 |
[2023-10-22 14:41:21,493::train::INFO] [train] Iter 558695 | loss 0.8527 | loss(rot) 0.8112 | loss(pos) 0.0379 | loss(seq) 0.0036 | grad 4.2098 | lr 0.0000 | time_forward 3.1210 | time_backward 4.2650 |
[2023-10-22 14:41:24,488::train::INFO] [train] Iter 558696 | loss 0.2177 | loss(rot) 0.1305 | loss(pos) 0.0170 | loss(seq) 0.0703 | grad 2.0540 | lr 0.0000 | time_forward 1.3850 | time_backward 1.6070 |
[2023-10-22 14:41:35,731::train::INFO] [train] Iter 558697 | loss 0.3310 | loss(rot) 0.1969 | loss(pos) 0.0213 | loss(seq) 0.1128 | grad 1.9833 | lr 0.0000 | time_forward 4.6990 | time_backward 6.5410 |
[2023-10-22 14:41:47,185::train::INFO] [train] Iter 558698 | loss 1.9908 | loss(rot) 1.4142 | loss(pos) 0.1015 | loss(seq) 0.4752 | grad 3.0181 | lr 0.0000 | time_forward 4.6710 | time_backward 6.7800 |
[2023-10-22 14:41:56,669::train::INFO] [train] Iter 558699 | loss 0.6116 | loss(rot) 0.1876 | loss(pos) 0.0480 | loss(seq) 0.3760 | grad 3.0060 | lr 0.0000 | time_forward 4.1400 | time_backward 5.3390 |
[2023-10-22 14:42:07,804::train::INFO] [train] Iter 558700 | loss 0.2087 | loss(rot) 0.1104 | loss(pos) 0.0630 | loss(seq) 0.0353 | grad 1.9696 | lr 0.0000 | time_forward 4.3700 | time_backward 6.7610 |
[2023-10-22 14:42:15,841::train::INFO] [train] Iter 558701 | loss 0.7491 | loss(rot) 0.4571 | loss(pos) 0.0360 | loss(seq) 0.2560 | grad 3.9636 | lr 0.0000 | time_forward 3.3960 | time_backward 4.6370 |
[2023-10-22 14:42:26,260::train::INFO] [train] Iter 558702 | loss 0.4992 | loss(rot) 0.1943 | loss(pos) 0.1336 | loss(seq) 0.1713 | grad 3.5145 | lr 0.0000 | time_forward 4.4240 | time_backward 5.9930 |
[2023-10-22 14:42:29,244::train::INFO] [train] Iter 558703 | loss 3.1518 | loss(rot) 0.0015 | loss(pos) 3.1503 | loss(seq) 0.0000 | grad 24.2967 | lr 0.0000 | time_forward 1.3830 | time_backward 1.5970 |
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