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[2023-10-25 13:47:22,020::train::INFO] [train] Iter 596456 | loss 0.7089 | loss(rot) 0.4437 | loss(pos) 0.0213 | loss(seq) 0.2439 | grad 53.5833 | lr 0.0000 | time_forward 4.0120 | time_backward 5.9310 |
[2023-10-25 13:47:31,379::train::INFO] [train] Iter 596457 | loss 1.2254 | loss(rot) 0.0075 | loss(pos) 1.2177 | loss(seq) 0.0002 | grad 16.1182 | lr 0.0000 | time_forward 3.8930 | time_backward 5.4630 |
[2023-10-25 13:47:34,278::train::INFO] [train] Iter 596458 | loss 0.3840 | loss(rot) 0.3364 | loss(pos) 0.0471 | loss(seq) 0.0005 | grad 1.9103 | lr 0.0000 | time_forward 1.3680 | time_backward 1.5280 |
[2023-10-25 13:47:37,114::train::INFO] [train] Iter 596459 | loss 0.3479 | loss(rot) 0.2994 | loss(pos) 0.0215 | loss(seq) 0.0270 | grad 30.6409 | lr 0.0000 | time_forward 1.3830 | time_backward 1.4490 |
[2023-10-25 13:47:45,492::train::INFO] [train] Iter 596460 | loss 0.7533 | loss(rot) 0.5713 | loss(pos) 0.1156 | loss(seq) 0.0664 | grad 2.1897 | lr 0.0000 | time_forward 3.4910 | time_backward 4.8550 |
[2023-10-25 13:47:52,177::train::INFO] [train] Iter 596461 | loss 0.2071 | loss(rot) 0.0684 | loss(pos) 0.0358 | loss(seq) 0.1029 | grad 2.3937 | lr 0.0000 | time_forward 2.8110 | time_backward 3.8710 |
[2023-10-25 13:47:55,035::train::INFO] [train] Iter 596462 | loss 0.6839 | loss(rot) 0.5336 | loss(pos) 0.0173 | loss(seq) 0.1330 | grad 4.1415 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4970 |
[2023-10-25 13:48:03,514::train::INFO] [train] Iter 596463 | loss 0.7644 | loss(rot) 0.5717 | loss(pos) 0.0418 | loss(seq) 0.1509 | grad 3.3683 | lr 0.0000 | time_forward 3.4730 | time_backward 4.9770 |
[2023-10-25 13:48:13,980::train::INFO] [train] Iter 596464 | loss 0.2995 | loss(rot) 0.1248 | loss(pos) 0.0535 | loss(seq) 0.1211 | grad 2.7901 | lr 0.0000 | time_forward 4.5990 | time_backward 5.8640 |
[2023-10-25 13:48:22,236::train::INFO] [train] Iter 596465 | loss 1.0657 | loss(rot) 0.8275 | loss(pos) 0.0273 | loss(seq) 0.2109 | grad 3.6702 | lr 0.0000 | time_forward 3.3530 | time_backward 4.9000 |
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