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[2023-10-25 11:22:37,136::train::INFO] [train] Iter 595257 | loss 1.1848 | loss(rot) 1.0688 | loss(pos) 0.0546 | loss(seq) 0.0614 | grad 49.8383 | lr 0.0000 | time_forward 1.4970 | time_backward 2.0270 |
[2023-10-25 11:22:47,269::train::INFO] [train] Iter 595258 | loss 1.0654 | loss(rot) 0.7370 | loss(pos) 0.0355 | loss(seq) 0.2928 | grad 6.3187 | lr 0.0000 | time_forward 4.1900 | time_backward 5.9250 |
[2023-10-25 11:22:57,226::train::INFO] [train] Iter 595259 | loss 1.9497 | loss(rot) 1.8470 | loss(pos) 0.1026 | loss(seq) 0.0001 | grad 11.6509 | lr 0.0000 | time_forward 4.0470 | time_backward 5.9080 |
[2023-10-25 11:23:05,957::train::INFO] [train] Iter 595260 | loss 0.1888 | loss(rot) 0.0369 | loss(pos) 0.0157 | loss(seq) 0.1362 | grad 1.5644 | lr 0.0000 | time_forward 3.6570 | time_backward 5.0700 |
[2023-10-25 11:23:14,407::train::INFO] [train] Iter 595261 | loss 0.5301 | loss(rot) 0.0992 | loss(pos) 0.0590 | loss(seq) 0.3719 | grad 2.7006 | lr 0.0000 | time_forward 3.5820 | time_backward 4.8650 |
[2023-10-25 11:23:22,685::train::INFO] [train] Iter 595262 | loss 0.4672 | loss(rot) 0.0385 | loss(pos) 0.4236 | loss(seq) 0.0051 | grad 6.4044 | lr 0.0000 | time_forward 3.4650 | time_backward 4.8100 |
[2023-10-25 11:23:25,549::train::INFO] [train] Iter 595263 | loss 0.7966 | loss(rot) 0.5078 | loss(pos) 0.0336 | loss(seq) 0.2552 | grad 4.6105 | lr 0.0000 | time_forward 1.3630 | time_backward 1.4970 |
[2023-10-25 11:23:28,496::train::INFO] [train] Iter 595264 | loss 0.4903 | loss(rot) 0.2323 | loss(pos) 0.0766 | loss(seq) 0.1814 | grad 4.2310 | lr 0.0000 | time_forward 1.4420 | time_backward 1.5020 |
[2023-10-25 11:23:37,406::train::INFO] [train] Iter 595265 | loss 0.2969 | loss(rot) 0.1379 | loss(pos) 0.0138 | loss(seq) 0.1453 | grad 3.0687 | lr 0.0000 | time_forward 3.8070 | time_backward 5.0990 |
[2023-10-25 11:23:47,354::train::INFO] [train] Iter 595266 | loss 1.7049 | loss(rot) 1.2782 | loss(pos) 0.0701 | loss(seq) 0.3567 | grad 5.3279 | lr 0.0000 | time_forward 4.0240 | time_backward 5.9210 |
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