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[2023-10-25 02:56:51,061::train::INFO] [train] Iter 590762 | loss 0.7816 | loss(rot) 0.7573 | loss(pos) 0.0174 | loss(seq) 0.0068 | grad 4.3554 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4330 |
[2023-10-25 02:56:59,444::train::INFO] [train] Iter 590763 | loss 0.8860 | loss(rot) 0.0757 | loss(pos) 0.8088 | loss(seq) 0.0016 | grad 8.7837 | lr 0.0000 | time_forward 3.6090 | time_backward 4.7720 |
[2023-10-25 02:57:07,911::train::INFO] [train] Iter 590764 | loss 0.3492 | loss(rot) 0.0751 | loss(pos) 0.2631 | loss(seq) 0.0110 | grad 6.6922 | lr 0.0000 | time_forward 3.5950 | time_backward 4.8690 |
[2023-10-25 02:57:16,306::train::INFO] [train] Iter 590765 | loss 0.4117 | loss(rot) 0.0657 | loss(pos) 0.0171 | loss(seq) 0.3289 | grad 2.2214 | lr 0.0000 | time_forward 3.6300 | time_backward 4.7610 |
[2023-10-25 02:57:25,388::train::INFO] [train] Iter 590766 | loss 2.1029 | loss(rot) 1.9245 | loss(pos) 0.0969 | loss(seq) 0.0816 | grad 5.2322 | lr 0.0000 | time_forward 3.8290 | time_backward 5.2510 |
[2023-10-25 02:57:32,255::train::INFO] [train] Iter 590767 | loss 1.0523 | loss(rot) 0.5757 | loss(pos) 0.0448 | loss(seq) 0.4318 | grad 4.1796 | lr 0.0000 | time_forward 2.9640 | time_backward 3.8990 |
[2023-10-25 02:57:39,731::train::INFO] [train] Iter 590768 | loss 0.5217 | loss(rot) 0.1656 | loss(pos) 0.0380 | loss(seq) 0.3181 | grad 2.4892 | lr 0.0000 | time_forward 3.2510 | time_backward 4.2220 |
[2023-10-25 02:57:47,117::train::INFO] [train] Iter 590769 | loss 0.6954 | loss(rot) 0.3843 | loss(pos) 0.0852 | loss(seq) 0.2259 | grad 4.4272 | lr 0.0000 | time_forward 3.1360 | time_backward 4.2460 |
[2023-10-25 02:57:55,977::train::INFO] [train] Iter 590770 | loss 0.6232 | loss(rot) 0.1162 | loss(pos) 0.0335 | loss(seq) 0.4735 | grad 2.5860 | lr 0.0000 | time_forward 3.6160 | time_backward 5.2400 |
[2023-10-25 02:58:02,655::train::INFO] [train] Iter 590771 | loss 1.1969 | loss(rot) 0.0297 | loss(pos) 1.1556 | loss(seq) 0.0115 | grad 11.8142 | lr 0.0000 | time_forward 2.9030 | time_backward 3.7720 |
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