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[2023-10-23 02:32:28,009::train::INFO] [train] Iter 565086 | loss 0.3733 | loss(rot) 0.0494 | loss(pos) 0.0449 | loss(seq) 0.2790 | grad 2.2994 | lr 0.0000 | time_forward 1.2820 | time_backward 1.4860 |
[2023-10-23 02:32:34,469::train::INFO] [train] Iter 565087 | loss 1.5519 | loss(rot) 1.4960 | loss(pos) 0.0293 | loss(seq) 0.0267 | grad 4.9362 | lr 0.0000 | time_forward 2.7770 | time_backward 3.6810 |
[2023-10-23 02:32:41,727::train::INFO] [train] Iter 565088 | loss 1.2535 | loss(rot) 1.2222 | loss(pos) 0.0200 | loss(seq) 0.0114 | grad 19.6597 | lr 0.0000 | time_forward 3.1220 | time_backward 4.1320 |
[2023-10-23 02:32:49,112::train::INFO] [train] Iter 565089 | loss 0.6635 | loss(rot) 0.0237 | loss(pos) 0.6350 | loss(seq) 0.0049 | grad 9.2107 | lr 0.0000 | time_forward 3.2000 | time_backward 4.1830 |
[2023-10-23 02:32:55,786::train::INFO] [train] Iter 565090 | loss 2.1593 | loss(rot) 1.7694 | loss(pos) 0.1233 | loss(seq) 0.2667 | grad 4.8966 | lr 0.0000 | time_forward 2.9760 | time_backward 3.6940 |
[2023-10-23 02:32:58,398::train::INFO] [train] Iter 565091 | loss 0.1312 | loss(rot) 0.1123 | loss(pos) 0.0188 | loss(seq) 0.0001 | grad 1.3573 | lr 0.0000 | time_forward 1.2430 | time_backward 1.3660 |
[2023-10-23 02:33:04,925::train::INFO] [train] Iter 565092 | loss 1.1081 | loss(rot) 0.3892 | loss(pos) 0.0408 | loss(seq) 0.6781 | grad 3.7804 | lr 0.0000 | time_forward 2.8130 | time_backward 3.7110 |
[2023-10-23 02:33:12,250::train::INFO] [train] Iter 565093 | loss 0.7219 | loss(rot) 0.3812 | loss(pos) 0.1040 | loss(seq) 0.2368 | grad 4.9480 | lr 0.0000 | time_forward 3.1660 | time_backward 4.1560 |
[2023-10-23 02:33:20,233::train::INFO] [train] Iter 565094 | loss 1.0789 | loss(rot) 0.7782 | loss(pos) 0.1279 | loss(seq) 0.1728 | grad 4.3679 | lr 0.0000 | time_forward 3.3330 | time_backward 4.6460 |
[2023-10-23 02:33:26,898::train::INFO] [train] Iter 565095 | loss 0.8354 | loss(rot) 0.6775 | loss(pos) 0.0596 | loss(seq) 0.0983 | grad 4.1351 | lr 0.0000 | time_forward 2.9430 | time_backward 3.7190 |
[2023-10-23 02:33:33,356::train::INFO] [train] Iter 565096 | loss 0.2383 | loss(rot) 0.1284 | loss(pos) 0.0157 | loss(seq) 0.0943 | grad 2.9145 | lr 0.0000 | time_forward 2.7120 | time_backward 3.7420 |
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