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[2023-10-24 15:58:21,180::train::INFO] [train] Iter 584968 | loss 0.5451 | loss(rot) 0.1338 | loss(pos) 0.3535 | loss(seq) 0.0578 | grad 5.2759 | lr 0.0000 | time_forward 3.4300 | time_backward 4.5100 |
[2023-10-24 15:58:23,927::train::INFO] [train] Iter 584969 | loss 1.1908 | loss(rot) 0.9533 | loss(pos) 0.0233 | loss(seq) 0.2142 | grad 4.9050 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4710 |
[2023-10-24 15:58:30,953::train::INFO] [train] Iter 584970 | loss 0.1260 | loss(rot) 0.1109 | loss(pos) 0.0101 | loss(seq) 0.0049 | grad 1.8086 | lr 0.0000 | time_forward 3.0140 | time_backward 3.9930 |
[2023-10-24 15:58:37,757::train::INFO] [train] Iter 584971 | loss 0.5317 | loss(rot) 0.4897 | loss(pos) 0.0107 | loss(seq) 0.0313 | grad 50.8944 | lr 0.0000 | time_forward 2.9280 | time_backward 3.8730 |
[2023-10-24 15:58:45,062::train::INFO] [train] Iter 584972 | loss 0.3103 | loss(rot) 0.2682 | loss(pos) 0.0309 | loss(seq) 0.0112 | grad 2.4313 | lr 0.0000 | time_forward 3.1450 | time_backward 4.1560 |
[2023-10-24 15:58:53,252::train::INFO] [train] Iter 584973 | loss 0.9392 | loss(rot) 0.8448 | loss(pos) 0.0331 | loss(seq) 0.0613 | grad 4.4454 | lr 0.0000 | time_forward 3.3880 | time_backward 4.7980 |
[2023-10-24 15:58:55,758::train::INFO] [train] Iter 584974 | loss 2.1180 | loss(rot) 1.4773 | loss(pos) 0.1280 | loss(seq) 0.5127 | grad 5.6514 | lr 0.0000 | time_forward 1.1750 | time_backward 1.3280 |
[2023-10-24 15:59:03,408::train::INFO] [train] Iter 584975 | loss 0.2056 | loss(rot) 0.1860 | loss(pos) 0.0071 | loss(seq) 0.0125 | grad 3.5489 | lr 0.0000 | time_forward 3.2350 | time_backward 4.4120 |
[2023-10-24 15:59:12,112::train::INFO] [train] Iter 584976 | loss 1.5750 | loss(rot) 1.5446 | loss(pos) 0.0224 | loss(seq) 0.0080 | grad 4.6647 | lr 0.0000 | time_forward 3.3220 | time_backward 5.3800 |
[2023-10-24 15:59:20,408::train::INFO] [train] Iter 584977 | loss 1.8178 | loss(rot) 1.7900 | loss(pos) 0.0277 | loss(seq) 0.0000 | grad 12.1186 | lr 0.0000 | time_forward 3.5420 | time_backward 4.7510 |
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