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[2023-09-03 00:43:34,479::train::INFO] [train] Iter 15970 | loss 1.2358 | loss(rot) 0.5585 | loss(pos) 0.5766 | loss(seq) 0.1006 | grad 4.3442 | lr 0.0010 | time_forward 3.5900 | time_backward 5.2560 |
[2023-09-03 00:43:42,658::train::INFO] [train] Iter 15971 | loss 2.5023 | loss(rot) 2.1163 | loss(pos) 0.1396 | loss(seq) 0.2464 | grad 5.0556 | lr 0.0010 | time_forward 3.4430 | time_backward 4.7330 |
[2023-09-03 00:43:49,381::train::INFO] [train] Iter 15972 | loss 0.8909 | loss(rot) 0.1940 | loss(pos) 0.4533 | loss(seq) 0.2436 | grad 5.0327 | lr 0.0010 | time_forward 2.9620 | time_backward 3.7570 |
[2023-09-03 00:43:52,209::train::INFO] [train] Iter 15973 | loss 2.2875 | loss(rot) 1.9179 | loss(pos) 0.1484 | loss(seq) 0.2212 | grad 4.6940 | lr 0.0010 | time_forward 1.2770 | time_backward 1.5480 |
[2023-09-03 00:44:01,593::train::INFO] [train] Iter 15974 | loss 1.6269 | loss(rot) 0.8217 | loss(pos) 0.3217 | loss(seq) 0.4835 | grad 3.5743 | lr 0.0010 | time_forward 4.0040 | time_backward 5.3760 |
[2023-09-03 00:44:11,686::train::INFO] [train] Iter 15975 | loss 1.4674 | loss(rot) 0.6161 | loss(pos) 0.7690 | loss(seq) 0.0822 | grad 4.2973 | lr 0.0010 | time_forward 4.1670 | time_backward 5.9230 |
[2023-09-03 00:44:15,201::train::INFO] [train] Iter 15976 | loss 1.7423 | loss(rot) 0.6269 | loss(pos) 0.6583 | loss(seq) 0.4571 | grad 4.7019 | lr 0.0010 | time_forward 1.5120 | time_backward 1.9980 |
[2023-09-03 00:44:25,251::train::INFO] [train] Iter 15977 | loss 1.6721 | loss(rot) 1.0970 | loss(pos) 0.1867 | loss(seq) 0.3884 | grad 6.6873 | lr 0.0010 | time_forward 4.0120 | time_backward 6.0340 |
[2023-09-03 00:44:33,939::train::INFO] [train] Iter 15978 | loss 0.9449 | loss(rot) 0.8235 | loss(pos) 0.1202 | loss(seq) 0.0012 | grad 5.3987 | lr 0.0010 | time_forward 3.4890 | time_backward 5.1950 |
[2023-09-03 00:44:36,417::train::INFO] [train] Iter 15979 | loss 1.0107 | loss(rot) 0.4312 | loss(pos) 0.1737 | loss(seq) 0.4058 | grad 3.7939 | lr 0.0010 | time_forward 1.1380 | time_backward 1.3370 |
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