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[2023-09-02 06:19:01,916::train::INFO] [train] Iter 06879 | loss 1.6745 | loss(rot) 0.9527 | loss(pos) 0.2222 | loss(seq) 0.4995 | grad 4.3804 | lr 0.0010 | time_forward 3.9950 | time_backward 5.9130 |
[2023-09-02 06:19:11,786::train::INFO] [train] Iter 06880 | loss 1.6375 | loss(rot) 1.4311 | loss(pos) 0.1881 | loss(seq) 0.0183 | grad 5.9119 | lr 0.0010 | time_forward 4.3810 | time_backward 5.4860 |
[2023-09-02 06:19:20,338::train::INFO] [train] Iter 06881 | loss 1.1226 | loss(rot) 0.5992 | loss(pos) 0.0894 | loss(seq) 0.4340 | grad 3.3787 | lr 0.0010 | time_forward 3.5880 | time_backward 4.9610 |
[2023-09-02 06:19:28,961::train::INFO] [train] Iter 06882 | loss 1.4772 | loss(rot) 0.9054 | loss(pos) 0.1567 | loss(seq) 0.4151 | grad 4.0434 | lr 0.0010 | time_forward 3.6800 | time_backward 4.9400 |
[2023-09-02 06:19:39,124::train::INFO] [train] Iter 06883 | loss 2.2577 | loss(rot) 1.5567 | loss(pos) 0.1955 | loss(seq) 0.5055 | grad 3.9620 | lr 0.0010 | time_forward 4.2590 | time_backward 5.9010 |
[2023-09-02 06:19:49,451::train::INFO] [train] Iter 06884 | loss 2.8639 | loss(rot) 2.3540 | loss(pos) 0.1916 | loss(seq) 0.3183 | grad 3.7888 | lr 0.0010 | time_forward 4.2560 | time_backward 6.0680 |
[2023-09-02 06:19:59,561::train::INFO] [train] Iter 06885 | loss 2.6636 | loss(rot) 1.7183 | loss(pos) 0.4565 | loss(seq) 0.4889 | grad 4.0846 | lr 0.0010 | time_forward 4.3510 | time_backward 5.7550 |
[2023-09-02 06:20:08,919::train::INFO] [train] Iter 06886 | loss 1.1892 | loss(rot) 0.0556 | loss(pos) 1.1230 | loss(seq) 0.0106 | grad 4.4454 | lr 0.0010 | time_forward 3.8620 | time_backward 5.4930 |
[2023-09-02 06:20:11,566::train::INFO] [train] Iter 06887 | loss 2.5419 | loss(rot) 1.5617 | loss(pos) 0.4952 | loss(seq) 0.4850 | grad 5.7159 | lr 0.0010 | time_forward 1.2480 | time_backward 1.3950 |
[2023-09-02 06:20:14,856::train::INFO] [train] Iter 06888 | loss 1.0442 | loss(rot) 0.3200 | loss(pos) 0.4241 | loss(seq) 0.3001 | grad 2.5383 | lr 0.0010 | time_forward 1.4340 | time_backward 1.8270 |
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