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[2023-10-23 05:11:55,843::train::INFO] [train] Iter 566686 | loss 0.9603 | loss(rot) 0.4698 | loss(pos) 0.0692 | loss(seq) 0.4213 | grad 3.7196 | lr 0.0000 | time_forward 3.3300 | time_backward 4.6400 |
[2023-10-23 05:12:02,415::train::INFO] [train] Iter 566687 | loss 0.2814 | loss(rot) 0.0536 | loss(pos) 0.1098 | loss(seq) 0.1181 | grad 3.2793 | lr 0.0000 | time_forward 2.8240 | time_backward 3.7460 |
[2023-10-23 05:12:04,616::train::INFO] [train] Iter 566688 | loss 0.2345 | loss(rot) 0.1063 | loss(pos) 0.0891 | loss(seq) 0.0391 | grad 2.7865 | lr 0.0000 | time_forward 1.0100 | time_backward 1.1870 |
[2023-10-23 05:12:12,010::train::INFO] [train] Iter 566689 | loss 1.8738 | loss(rot) 1.4229 | loss(pos) 0.1093 | loss(seq) 0.3416 | grad 3.2478 | lr 0.0000 | time_forward 3.2190 | time_backward 4.1720 |
[2023-10-23 05:12:20,060::train::INFO] [train] Iter 566690 | loss 1.6359 | loss(rot) 1.3071 | loss(pos) 0.0540 | loss(seq) 0.2748 | grad 3.6788 | lr 0.0000 | time_forward 3.3310 | time_backward 4.7160 |
[2023-10-23 05:12:22,700::train::INFO] [train] Iter 566691 | loss 0.4825 | loss(rot) 0.2731 | loss(pos) 0.0202 | loss(seq) 0.1892 | grad 2.8392 | lr 0.0000 | time_forward 1.2560 | time_backward 1.3800 |
[2023-10-23 05:12:24,913::train::INFO] [train] Iter 566692 | loss 0.8875 | loss(rot) 0.8379 | loss(pos) 0.0422 | loss(seq) 0.0074 | grad 25.0836 | lr 0.0000 | time_forward 1.0200 | time_backward 1.1780 |
[2023-10-23 05:12:32,884::train::INFO] [train] Iter 566693 | loss 0.2803 | loss(rot) 0.0945 | loss(pos) 0.0527 | loss(seq) 0.1331 | grad 1.8823 | lr 0.0000 | time_forward 3.3140 | time_backward 4.6540 |
[2023-10-23 05:12:40,206::train::INFO] [train] Iter 566694 | loss 1.1257 | loss(rot) 0.6791 | loss(pos) 0.0920 | loss(seq) 0.3546 | grad 3.7507 | lr 0.0000 | time_forward 3.2000 | time_backward 4.1180 |
[2023-10-23 05:12:42,865::train::INFO] [train] Iter 566695 | loss 0.7846 | loss(rot) 0.7569 | loss(pos) 0.0169 | loss(seq) 0.0109 | grad 5.2112 | lr 0.0000 | time_forward 1.2590 | time_backward 1.3980 |
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