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[2023-10-25 13:22:17,525::train::INFO] [train] Iter 596256 | loss 1.7953 | loss(rot) 1.0814 | loss(pos) 0.2642 | loss(seq) 0.4496 | grad 5.9661 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4960 |
[2023-10-25 13:22:20,284::train::INFO] [train] Iter 596257 | loss 0.3011 | loss(rot) 0.0234 | loss(pos) 0.2649 | loss(seq) 0.0128 | grad 7.1466 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4240 |
[2023-10-25 13:22:30,051::train::INFO] [train] Iter 596258 | loss 0.6416 | loss(rot) 0.1080 | loss(pos) 0.0346 | loss(seq) 0.4990 | grad 2.7159 | lr 0.0000 | time_forward 3.9940 | time_backward 5.7490 |
[2023-10-25 13:22:37,168::train::INFO] [train] Iter 596259 | loss 0.6446 | loss(rot) 0.1394 | loss(pos) 0.0358 | loss(seq) 0.4694 | grad 3.6657 | lr 0.0000 | time_forward 2.9760 | time_backward 4.1380 |
[2023-10-25 13:22:47,144::train::INFO] [train] Iter 596260 | loss 0.4923 | loss(rot) 0.1936 | loss(pos) 0.0697 | loss(seq) 0.2290 | grad 3.7261 | lr 0.0000 | time_forward 4.0370 | time_backward 5.9370 |
[2023-10-25 13:22:57,517::train::INFO] [train] Iter 596261 | loss 0.8252 | loss(rot) 0.3116 | loss(pos) 0.0616 | loss(seq) 0.4520 | grad 5.6592 | lr 0.0000 | time_forward 4.2320 | time_backward 6.1370 |
[2023-10-25 13:23:07,155::train::INFO] [train] Iter 596262 | loss 0.5603 | loss(rot) 0.5101 | loss(pos) 0.0306 | loss(seq) 0.0195 | grad 2.1641 | lr 0.0000 | time_forward 4.3960 | time_backward 5.2390 |
[2023-10-25 13:23:10,018::train::INFO] [train] Iter 596263 | loss 0.3419 | loss(rot) 0.0794 | loss(pos) 0.2367 | loss(seq) 0.0258 | grad 5.3100 | lr 0.0000 | time_forward 1.3680 | time_backward 1.4920 |
[2023-10-25 13:23:18,756::train::INFO] [train] Iter 596264 | loss 1.7694 | loss(rot) 0.0955 | loss(pos) 1.6738 | loss(seq) 0.0001 | grad 13.2589 | lr 0.0000 | time_forward 3.5320 | time_backward 5.1590 |
[2023-10-25 13:23:21,638::train::INFO] [train] Iter 596265 | loss 0.8821 | loss(rot) 0.4283 | loss(pos) 0.0379 | loss(seq) 0.4159 | grad 3.5582 | lr 0.0000 | time_forward 1.2770 | time_backward 1.6010 |
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