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[2023-10-23 19:16:33,040::train::INFO] [train] Iter 574478 | loss 0.9059 | loss(rot) 0.1266 | loss(pos) 0.5750 | loss(seq) 0.2043 | grad 3.7408 | lr 0.0000 | time_forward 4.1170 | time_backward 6.3420 |
[2023-10-23 19:16:41,196::train::INFO] [train] Iter 574479 | loss 1.6559 | loss(rot) 1.0559 | loss(pos) 0.2986 | loss(seq) 0.3013 | grad 4.7292 | lr 0.0000 | time_forward 3.3940 | time_backward 4.7600 |
[2023-10-23 19:16:51,164::train::INFO] [train] Iter 574480 | loss 0.7251 | loss(rot) 0.3138 | loss(pos) 0.3860 | loss(seq) 0.0253 | grad 4.1357 | lr 0.0000 | time_forward 4.0520 | time_backward 5.9120 |
[2023-10-23 19:16:57,786::train::INFO] [train] Iter 574481 | loss 0.5509 | loss(rot) 0.4818 | loss(pos) 0.0260 | loss(seq) 0.0432 | grad 41.8555 | lr 0.0000 | time_forward 2.9240 | time_backward 3.6930 |
[2023-10-23 19:17:06,905::train::INFO] [train] Iter 574482 | loss 0.7408 | loss(rot) 0.6220 | loss(pos) 0.0318 | loss(seq) 0.0870 | grad 3.9159 | lr 0.0000 | time_forward 3.9040 | time_backward 5.1990 |
[2023-10-23 19:17:15,623::train::INFO] [train] Iter 574483 | loss 0.6331 | loss(rot) 0.3023 | loss(pos) 0.0213 | loss(seq) 0.3095 | grad 3.7923 | lr 0.0000 | time_forward 3.7420 | time_backward 4.9730 |
[2023-10-23 19:17:18,405::train::INFO] [train] Iter 574484 | loss 0.4023 | loss(rot) 0.1554 | loss(pos) 0.0261 | loss(seq) 0.2208 | grad 2.5291 | lr 0.0000 | time_forward 1.3490 | time_backward 1.4300 |
[2023-10-23 19:17:26,741::train::INFO] [train] Iter 574485 | loss 0.2552 | loss(rot) 0.1509 | loss(pos) 0.0164 | loss(seq) 0.0879 | grad 2.1494 | lr 0.0000 | time_forward 3.5550 | time_backward 4.7780 |
[2023-10-23 19:17:36,581::train::INFO] [train] Iter 574486 | loss 0.9870 | loss(rot) 0.5650 | loss(pos) 0.2682 | loss(seq) 0.1538 | grad 6.1144 | lr 0.0000 | time_forward 4.0000 | time_backward 5.8360 |
[2023-10-23 19:17:46,370::train::INFO] [train] Iter 574487 | loss 0.6229 | loss(rot) 0.0156 | loss(pos) 0.6043 | loss(seq) 0.0030 | grad 3.6820 | lr 0.0000 | time_forward 4.0230 | time_backward 5.7630 |
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