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[2023-10-25 06:23:39,510::train::INFO] [train] Iter 592660 | loss 1.2617 | loss(rot) 0.4564 | loss(pos) 0.4548 | loss(seq) 0.3505 | grad 5.7064 | lr 0.0000 | time_forward 3.5500 | time_backward 5.1650 |
[2023-10-25 06:23:42,667::train::INFO] [train] Iter 592661 | loss 0.5214 | loss(rot) 0.1527 | loss(pos) 0.3620 | loss(seq) 0.0067 | grad 4.8560 | lr 0.0000 | time_forward 1.4070 | time_backward 1.7470 |
[2023-10-25 06:23:45,400::train::INFO] [train] Iter 592662 | loss 0.5300 | loss(rot) 0.2912 | loss(pos) 0.0444 | loss(seq) 0.1944 | grad 4.1860 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4270 |
[2023-10-25 06:23:53,447::train::INFO] [train] Iter 592663 | loss 0.7568 | loss(rot) 0.0225 | loss(pos) 0.7323 | loss(seq) 0.0020 | grad 14.1972 | lr 0.0000 | time_forward 3.4580 | time_backward 4.5860 |
[2023-10-25 06:24:02,191::train::INFO] [train] Iter 592664 | loss 0.3398 | loss(rot) 0.1124 | loss(pos) 0.0711 | loss(seq) 0.1563 | grad 2.2074 | lr 0.0000 | time_forward 3.7090 | time_backward 5.0320 |
[2023-10-25 06:24:04,884::train::INFO] [train] Iter 592665 | loss 0.5732 | loss(rot) 0.2771 | loss(pos) 0.0638 | loss(seq) 0.2323 | grad 3.1597 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4170 |
[2023-10-25 06:24:12,352::train::INFO] [train] Iter 592666 | loss 0.4158 | loss(rot) 0.2045 | loss(pos) 0.1108 | loss(seq) 0.1005 | grad 3.9046 | lr 0.0000 | time_forward 3.1690 | time_backward 4.2970 |
[2023-10-25 06:24:14,611::train::INFO] [train] Iter 592667 | loss 0.1297 | loss(rot) 0.0829 | loss(pos) 0.0213 | loss(seq) 0.0255 | grad 1.8711 | lr 0.0000 | time_forward 1.0290 | time_backward 1.2260 |
[2023-10-25 06:24:16,843::train::INFO] [train] Iter 592668 | loss 0.7530 | loss(rot) 0.1982 | loss(pos) 0.1068 | loss(seq) 0.4481 | grad 3.3856 | lr 0.0000 | time_forward 1.0120 | time_backward 1.2180 |
[2023-10-25 06:24:23,977::train::INFO] [train] Iter 592669 | loss 0.9827 | loss(rot) 0.6622 | loss(pos) 0.0493 | loss(seq) 0.2712 | grad 4.8622 | lr 0.0000 | time_forward 3.0070 | time_backward 4.1230 |
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