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[2023-10-25 12:21:50,585::train::INFO] [train] Iter 595757 | loss 0.4350 | loss(rot) 0.4059 | loss(pos) 0.0171 | loss(seq) 0.0120 | grad 4.0337 | lr 0.0000 | time_forward 3.5160 | time_backward 4.7990 |
[2023-10-25 12:21:59,308::train::INFO] [train] Iter 595758 | loss 0.7068 | loss(rot) 0.2673 | loss(pos) 0.0579 | loss(seq) 0.3816 | grad 3.1280 | lr 0.0000 | time_forward 3.6630 | time_backward 5.0570 |
[2023-10-25 12:22:07,635::train::INFO] [train] Iter 595759 | loss 0.2951 | loss(rot) 0.2272 | loss(pos) 0.0179 | loss(seq) 0.0500 | grad 2.8278 | lr 0.0000 | time_forward 3.4950 | time_backward 4.8280 |
[2023-10-25 12:22:10,402::train::INFO] [train] Iter 595760 | loss 0.0954 | loss(rot) 0.0541 | loss(pos) 0.0413 | loss(seq) 0.0000 | grad 1.8678 | lr 0.0000 | time_forward 1.3440 | time_backward 1.4190 |
[2023-10-25 12:22:13,192::train::INFO] [train] Iter 595761 | loss 1.0825 | loss(rot) 0.7880 | loss(pos) 0.0454 | loss(seq) 0.2491 | grad 4.2242 | lr 0.0000 | time_forward 1.3660 | time_backward 1.4210 |
[2023-10-25 12:22:23,417::train::INFO] [train] Iter 595762 | loss 1.0279 | loss(rot) 0.9724 | loss(pos) 0.0539 | loss(seq) 0.0016 | grad 3.2183 | lr 0.0000 | time_forward 4.3890 | time_backward 5.8310 |
[2023-10-25 12:22:31,355::train::INFO] [train] Iter 595763 | loss 0.5892 | loss(rot) 0.1046 | loss(pos) 0.1794 | loss(seq) 0.3051 | grad 3.2185 | lr 0.0000 | time_forward 3.3590 | time_backward 4.5760 |
[2023-10-25 12:22:41,244::train::INFO] [train] Iter 595764 | loss 0.5766 | loss(rot) 0.4599 | loss(pos) 0.0200 | loss(seq) 0.0967 | grad 2.8804 | lr 0.0000 | time_forward 3.9970 | time_backward 5.8890 |
[2023-10-25 12:22:51,207::train::INFO] [train] Iter 595765 | loss 0.6719 | loss(rot) 0.3793 | loss(pos) 0.2000 | loss(seq) 0.0926 | grad 7.7910 | lr 0.0000 | time_forward 4.0100 | time_backward 5.9500 |
[2023-10-25 12:22:54,034::train::INFO] [train] Iter 595766 | loss 0.1685 | loss(rot) 0.0639 | loss(pos) 0.0572 | loss(seq) 0.0474 | grad 2.4148 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4930 |
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