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[2023-09-02 11:13:05,598::train::INFO] [train] Iter 09276 | loss 0.7925 | loss(rot) 0.1839 | loss(pos) 0.3727 | loss(seq) 0.2359 | grad 3.8042 | lr 0.0010 | time_forward 3.8500 | time_backward 5.3670 |
[2023-09-02 11:13:14,220::train::INFO] [train] Iter 09277 | loss 0.6926 | loss(rot) 0.0765 | loss(pos) 0.3193 | loss(seq) 0.2968 | grad 4.3461 | lr 0.0010 | time_forward 3.6180 | time_backward 5.0000 |
[2023-09-02 11:13:21,824::train::INFO] [train] Iter 09278 | loss 2.1799 | loss(rot) 1.5021 | loss(pos) 0.1461 | loss(seq) 0.5317 | grad 2.9989 | lr 0.0010 | time_forward 3.2470 | time_backward 4.3530 |
[2023-09-02 11:13:31,948::train::INFO] [train] Iter 09279 | loss 1.6430 | loss(rot) 0.9820 | loss(pos) 0.2642 | loss(seq) 0.3967 | grad 4.3224 | lr 0.0010 | time_forward 4.1580 | time_backward 5.9620 |
[2023-09-02 11:13:42,312::train::INFO] [train] Iter 09280 | loss 2.0536 | loss(rot) 1.1025 | loss(pos) 0.3466 | loss(seq) 0.6046 | grad 4.4214 | lr 0.0010 | time_forward 4.2530 | time_backward 6.1070 |
[2023-09-02 11:13:47,242::train::INFO] [train] Iter 09281 | loss 1.2854 | loss(rot) 0.6850 | loss(pos) 0.1840 | loss(seq) 0.4164 | grad 4.3021 | lr 0.0010 | time_forward 2.0980 | time_backward 2.8210 |
[2023-09-02 11:13:50,742::train::INFO] [train] Iter 09282 | loss 1.2115 | loss(rot) 0.4396 | loss(pos) 0.7457 | loss(seq) 0.0263 | grad 2.7023 | lr 0.0010 | time_forward 1.4860 | time_backward 1.9670 |
[2023-09-02 11:13:53,560::train::INFO] [train] Iter 09283 | loss 2.2579 | loss(rot) 1.4841 | loss(pos) 0.2663 | loss(seq) 0.5075 | grad 4.4515 | lr 0.0010 | time_forward 1.3180 | time_backward 1.4970 |
[2023-09-02 11:13:56,454::train::INFO] [train] Iter 09284 | loss 1.5584 | loss(rot) 1.4145 | loss(pos) 0.1432 | loss(seq) 0.0007 | grad 4.1650 | lr 0.0010 | time_forward 1.3590 | time_backward 1.5300 |
[2023-09-02 11:13:59,212::train::INFO] [train] Iter 09285 | loss 1.6318 | loss(rot) 0.7642 | loss(pos) 0.3674 | loss(seq) 0.5002 | grad 4.2316 | lr 0.0010 | time_forward 1.3140 | time_backward 1.4410 |
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