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[2023-10-23 14:49:55,416::train::INFO] [train] Iter 572080 | loss 0.8985 | loss(rot) 0.5759 | loss(pos) 0.2473 | loss(seq) 0.0753 | grad 5.2222 | lr 0.0000 | time_forward 3.1810 | time_backward 5.0150 |
[2023-10-23 14:49:58,626::train::INFO] [train] Iter 572081 | loss 0.4416 | loss(rot) 0.0680 | loss(pos) 0.3666 | loss(seq) 0.0071 | grad 4.5718 | lr 0.0000 | time_forward 1.4600 | time_backward 1.7480 |
[2023-10-23 14:50:06,557::train::INFO] [train] Iter 572082 | loss 0.3377 | loss(rot) 0.1596 | loss(pos) 0.0367 | loss(seq) 0.1414 | grad 2.4387 | lr 0.0000 | time_forward 3.4390 | time_backward 4.4790 |
[2023-10-23 14:50:13,806::train::INFO] [train] Iter 572083 | loss 0.9208 | loss(rot) 0.9029 | loss(pos) 0.0169 | loss(seq) 0.0010 | grad 11.3004 | lr 0.0000 | time_forward 3.0940 | time_backward 4.1510 |
[2023-10-23 14:50:22,229::train::INFO] [train] Iter 572084 | loss 0.3991 | loss(rot) 0.2050 | loss(pos) 0.0271 | loss(seq) 0.1670 | grad 2.8045 | lr 0.0000 | time_forward 3.5190 | time_backward 4.9010 |
[2023-10-23 14:50:29,815::train::INFO] [train] Iter 572085 | loss 0.1391 | loss(rot) 0.1080 | loss(pos) 0.0310 | loss(seq) 0.0000 | grad 2.1042 | lr 0.0000 | time_forward 3.2010 | time_backward 4.3320 |
[2023-10-23 14:50:37,294::train::INFO] [train] Iter 572086 | loss 0.1453 | loss(rot) 0.1014 | loss(pos) 0.0438 | loss(seq) 0.0001 | grad 1.7837 | lr 0.0000 | time_forward 3.2510 | time_backward 4.2250 |
[2023-10-23 14:50:45,054::train::INFO] [train] Iter 572087 | loss 0.3572 | loss(rot) 0.3189 | loss(pos) 0.0242 | loss(seq) 0.0141 | grad 2.8133 | lr 0.0000 | time_forward 3.3550 | time_backward 4.4030 |
[2023-10-23 14:50:52,260::train::INFO] [train] Iter 572088 | loss 0.3133 | loss(rot) 0.1888 | loss(pos) 0.0302 | loss(seq) 0.0943 | grad 3.4298 | lr 0.0000 | time_forward 3.1320 | time_backward 4.0710 |
[2023-10-23 14:50:59,130::train::INFO] [train] Iter 572089 | loss 0.8455 | loss(rot) 0.5674 | loss(pos) 0.0394 | loss(seq) 0.2387 | grad 5.0698 | lr 0.0000 | time_forward 2.8810 | time_backward 3.9860 |
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