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[2023-10-25 20:28:37,325::train::INFO] [train] Iter 599953 | loss 1.2002 | loss(rot) 0.6835 | loss(pos) 0.1865 | loss(seq) 0.3302 | grad 5.5760 | lr 0.0000 | time_forward 3.9280 | time_backward 5.4400 |
[2023-10-25 20:28:39,676::train::INFO] [train] Iter 599954 | loss 0.3143 | loss(rot) 0.1369 | loss(pos) 0.0882 | loss(seq) 0.0892 | grad 3.4639 | lr 0.0000 | time_forward 1.0600 | time_backward 1.2890 |
[2023-10-25 20:28:48,592::train::INFO] [train] Iter 599955 | loss 1.7456 | loss(rot) 1.6262 | loss(pos) 0.0645 | loss(seq) 0.0550 | grad 13.0033 | lr 0.0000 | time_forward 3.7730 | time_backward 5.1400 |
[2023-10-25 20:28:57,897::train::INFO] [train] Iter 599956 | loss 1.1180 | loss(rot) 0.4393 | loss(pos) 0.4213 | loss(seq) 0.2574 | grad 4.5217 | lr 0.0000 | time_forward 3.9480 | time_backward 5.3540 |
[2023-10-25 20:29:05,991::train::INFO] [train] Iter 599957 | loss 0.8655 | loss(rot) 0.2460 | loss(pos) 0.6145 | loss(seq) 0.0050 | grad 9.9223 | lr 0.0000 | time_forward 3.4070 | time_backward 4.6830 |
[2023-10-25 20:29:08,810::train::INFO] [train] Iter 599958 | loss 1.0196 | loss(rot) 0.9460 | loss(pos) 0.0265 | loss(seq) 0.0471 | grad 4.9678 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4870 |
[2023-10-25 20:29:16,972::train::INFO] [train] Iter 599959 | loss 0.1302 | loss(rot) 0.0245 | loss(pos) 0.0957 | loss(seq) 0.0100 | grad 3.0600 | lr 0.0000 | time_forward 3.4520 | time_backward 4.7070 |
[2023-10-25 20:29:25,924::train::INFO] [train] Iter 599960 | loss 0.4073 | loss(rot) 0.1774 | loss(pos) 0.0170 | loss(seq) 0.2129 | grad 2.6664 | lr 0.0000 | time_forward 3.6790 | time_backward 5.2700 |
[2023-10-25 20:29:36,314::train::INFO] [train] Iter 599961 | loss 0.5309 | loss(rot) 0.4159 | loss(pos) 0.0376 | loss(seq) 0.0773 | grad 3.0938 | lr 0.0000 | time_forward 4.3510 | time_backward 6.0350 |
[2023-10-25 20:29:45,857::train::INFO] [train] Iter 599962 | loss 0.6408 | loss(rot) 0.2453 | loss(pos) 0.0304 | loss(seq) 0.3651 | grad 2.7829 | lr 0.0000 | time_forward 4.0450 | time_backward 5.4940 |
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