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[2023-09-02 07:45:57,353::train::INFO] [train] Iter 07578 | loss 1.5411 | loss(rot) 0.6808 | loss(pos) 0.6085 | loss(seq) 0.2518 | grad 3.1229 | lr 0.0010 | time_forward 4.0190 | time_backward 5.6620 |
[2023-09-02 07:45:59,551::train::INFO] [train] Iter 07579 | loss 1.7262 | loss(rot) 0.0183 | loss(pos) 1.7056 | loss(seq) 0.0023 | grad 7.8054 | lr 0.0010 | time_forward 1.0320 | time_backward 1.1620 |
[2023-09-02 07:46:10,390::train::INFO] [train] Iter 07580 | loss 1.6177 | loss(rot) 0.4841 | loss(pos) 0.3581 | loss(seq) 0.7755 | grad 4.2097 | lr 0.0010 | time_forward 4.7340 | time_backward 6.1010 |
[2023-09-02 07:46:19,254::train::INFO] [train] Iter 07581 | loss 1.6715 | loss(rot) 0.9959 | loss(pos) 0.1765 | loss(seq) 0.4991 | grad 3.9052 | lr 0.0010 | time_forward 3.6810 | time_backward 5.1790 |
[2023-09-02 07:46:21,968::train::INFO] [train] Iter 07582 | loss 2.4045 | loss(rot) 1.7688 | loss(pos) 0.1517 | loss(seq) 0.4840 | grad 4.9150 | lr 0.0010 | time_forward 1.2970 | time_backward 1.4140 |
[2023-09-02 07:46:31,953::train::INFO] [train] Iter 07583 | loss 1.9251 | loss(rot) 1.1968 | loss(pos) 0.2874 | loss(seq) 0.4410 | grad 4.0957 | lr 0.0010 | time_forward 4.0180 | time_backward 5.9640 |
[2023-09-02 07:46:40,929::train::INFO] [train] Iter 07584 | loss 2.0337 | loss(rot) 1.6208 | loss(pos) 0.3697 | loss(seq) 0.0431 | grad 6.4244 | lr 0.0010 | time_forward 3.8410 | time_backward 5.1310 |
[2023-09-02 07:46:44,147::train::INFO] [train] Iter 07585 | loss 2.6827 | loss(rot) 1.6444 | loss(pos) 0.5205 | loss(seq) 0.5178 | grad 5.8082 | lr 0.0010 | time_forward 1.4170 | time_backward 1.7980 |
[2023-09-02 07:46:53,358::train::INFO] [train] Iter 07586 | loss 1.3218 | loss(rot) 0.5502 | loss(pos) 0.4315 | loss(seq) 0.3401 | grad 2.8412 | lr 0.0010 | time_forward 3.8980 | time_backward 5.3090 |
[2023-09-02 07:46:56,135::train::INFO] [train] Iter 07587 | loss 1.6309 | loss(rot) 1.0144 | loss(pos) 0.2025 | loss(seq) 0.4140 | grad 7.2256 | lr 0.0010 | time_forward 1.3200 | time_backward 1.4540 |
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