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[2023-10-23 01:23:41,309::train::INFO] [train] Iter 564389 | loss 0.6996 | loss(rot) 0.6170 | loss(pos) 0.0353 | loss(seq) 0.0474 | grad 3.6449 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4520 |
[2023-10-23 01:23:46,440::train::INFO] [train] Iter 564390 | loss 0.0906 | loss(rot) 0.0718 | loss(pos) 0.0186 | loss(seq) 0.0002 | grad 1.6934 | lr 0.0000 | time_forward 2.2960 | time_backward 2.8310 |
[2023-10-23 01:23:54,392::train::INFO] [train] Iter 564391 | loss 0.8291 | loss(rot) 0.6489 | loss(pos) 0.0627 | loss(seq) 0.1175 | grad 4.1077 | lr 0.0000 | time_forward 3.3450 | time_backward 4.5890 |
[2023-10-23 01:23:57,075::train::INFO] [train] Iter 564392 | loss 0.7542 | loss(rot) 0.1644 | loss(pos) 0.5444 | loss(seq) 0.0454 | grad 5.3144 | lr 0.0000 | time_forward 1.2850 | time_backward 1.3950 |
[2023-10-23 01:24:04,526::train::INFO] [train] Iter 564393 | loss 0.5579 | loss(rot) 0.1291 | loss(pos) 0.1235 | loss(seq) 0.3053 | grad 2.9095 | lr 0.0000 | time_forward 3.2080 | time_backward 4.2410 |
[2023-10-23 01:24:07,679::train::INFO] [train] Iter 564394 | loss 0.2959 | loss(rot) 0.1906 | loss(pos) 0.0650 | loss(seq) 0.0403 | grad 8.9418 | lr 0.0000 | time_forward 1.4420 | time_backward 1.7070 |
[2023-10-23 01:24:15,822::train::INFO] [train] Iter 564395 | loss 0.5619 | loss(rot) 0.2677 | loss(pos) 0.0130 | loss(seq) 0.2812 | grad 2.6269 | lr 0.0000 | time_forward 3.3290 | time_backward 4.8020 |
[2023-10-23 01:24:22,903::train::INFO] [train] Iter 564396 | loss 0.3952 | loss(rot) 0.1057 | loss(pos) 0.2826 | loss(seq) 0.0070 | grad 4.5907 | lr 0.0000 | time_forward 2.9330 | time_backward 4.1440 |
[2023-10-23 01:24:26,007::train::INFO] [train] Iter 564397 | loss 2.1124 | loss(rot) 2.0952 | loss(pos) 0.0168 | loss(seq) 0.0003 | grad 4.5029 | lr 0.0000 | time_forward 1.3800 | time_backward 1.7210 |
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