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[2023-10-22 21:41:53,791::train::INFO] [train] Iter 562290 | loss 0.2617 | loss(rot) 0.0852 | loss(pos) 0.0290 | loss(seq) 0.1475 | grad 2.4989 | lr 0.0000 | time_forward 3.6590 | time_backward 5.2100 |
[2023-10-22 21:42:00,800::train::INFO] [train] Iter 562291 | loss 2.5874 | loss(rot) 2.3880 | loss(pos) 0.0696 | loss(seq) 0.1297 | grad 6.0134 | lr 0.0000 | time_forward 2.9750 | time_backward 4.0310 |
[2023-10-22 21:42:09,584::train::INFO] [train] Iter 562292 | loss 0.7471 | loss(rot) 0.3015 | loss(pos) 0.0986 | loss(seq) 0.3470 | grad 2.9817 | lr 0.0000 | time_forward 3.5620 | time_backward 5.2190 |
[2023-10-22 21:42:17,227::train::INFO] [train] Iter 562293 | loss 0.4553 | loss(rot) 0.4323 | loss(pos) 0.0227 | loss(seq) 0.0003 | grad 3.5151 | lr 0.0000 | time_forward 3.2730 | time_backward 4.3670 |
[2023-10-22 21:42:24,866::train::INFO] [train] Iter 562294 | loss 1.1363 | loss(rot) 0.7355 | loss(pos) 0.0661 | loss(seq) 0.3347 | grad 6.0994 | lr 0.0000 | time_forward 3.2680 | time_backward 4.3670 |
[2023-10-22 21:42:33,541::train::INFO] [train] Iter 562295 | loss 0.7845 | loss(rot) 0.5030 | loss(pos) 0.0598 | loss(seq) 0.2218 | grad 6.2709 | lr 0.0000 | time_forward 3.5630 | time_backward 5.1090 |
[2023-10-22 21:42:42,559::train::INFO] [train] Iter 562296 | loss 0.4652 | loss(rot) 0.0629 | loss(pos) 0.3914 | loss(seq) 0.0110 | grad 6.2353 | lr 0.0000 | time_forward 3.7100 | time_backward 5.3050 |
[2023-10-22 21:42:45,223::train::INFO] [train] Iter 562297 | loss 0.2520 | loss(rot) 0.0350 | loss(pos) 0.2134 | loss(seq) 0.0036 | grad 7.2631 | lr 0.0000 | time_forward 1.2920 | time_backward 1.3690 |
[2023-10-22 21:42:53,224::train::INFO] [train] Iter 562298 | loss 0.5541 | loss(rot) 0.4948 | loss(pos) 0.0560 | loss(seq) 0.0034 | grad 34.7505 | lr 0.0000 | time_forward 3.8940 | time_backward 4.0810 |
[2023-10-22 21:43:00,566::train::INFO] [train] Iter 562299 | loss 0.3468 | loss(rot) 0.1620 | loss(pos) 0.0468 | loss(seq) 0.1381 | grad 2.0726 | lr 0.0000 | time_forward 3.1180 | time_backward 4.2200 |
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