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[2023-09-01 20:47:10,705::train::INFO] [train] Iter 02183 | loss 2.8154 | loss(rot) 2.4791 | loss(pos) 0.2205 | loss(seq) 0.1158 | grad 3.6787 | lr 0.0010 | time_forward 3.9360 | time_backward 5.3570 |
[2023-09-01 20:47:20,855::train::INFO] [train] Iter 02184 | loss 3.1045 | loss(rot) 2.2617 | loss(pos) 0.4048 | loss(seq) 0.4380 | grad 4.0126 | lr 0.0010 | time_forward 4.0700 | time_backward 6.0750 |
[2023-09-01 20:47:29,659::train::INFO] [train] Iter 02185 | loss 2.4951 | loss(rot) 1.6110 | loss(pos) 0.3167 | loss(seq) 0.5674 | grad 4.9532 | lr 0.0010 | time_forward 3.6950 | time_backward 5.1060 |
[2023-09-01 20:47:39,710::train::INFO] [train] Iter 02186 | loss 3.3687 | loss(rot) 3.0685 | loss(pos) 0.2881 | loss(seq) 0.0121 | grad 2.8955 | lr 0.0010 | time_forward 4.0570 | time_backward 5.9900 |
[2023-09-01 20:47:48,324::train::INFO] [train] Iter 02187 | loss 1.4348 | loss(rot) 0.1794 | loss(pos) 1.2501 | loss(seq) 0.0052 | grad 5.8759 | lr 0.0010 | time_forward 3.7350 | time_backward 4.8750 |
[2023-09-01 20:47:56,497::train::INFO] [train] Iter 02188 | loss 1.5028 | loss(rot) 0.7508 | loss(pos) 0.3876 | loss(seq) 0.3644 | grad 3.8999 | lr 0.0010 | time_forward 3.4300 | time_backward 4.7390 |
[2023-09-01 20:48:06,510::train::INFO] [train] Iter 02189 | loss 2.0334 | loss(rot) 0.4913 | loss(pos) 1.5385 | loss(seq) 0.0036 | grad 5.2162 | lr 0.0010 | time_forward 4.1230 | time_backward 5.8880 |
[2023-09-01 20:48:09,220::train::INFO] [train] Iter 02190 | loss 1.8844 | loss(rot) 1.1766 | loss(pos) 0.2841 | loss(seq) 0.4237 | grad 4.7476 | lr 0.0010 | time_forward 1.2700 | time_backward 1.4240 |
[2023-09-01 20:48:17,978::train::INFO] [train] Iter 02191 | loss 2.8552 | loss(rot) 2.6188 | loss(pos) 0.2358 | loss(seq) 0.0007 | grad 3.9151 | lr 0.0010 | time_forward 3.6590 | time_backward 5.0650 |
[2023-09-01 20:48:25,314::train::INFO] [train] Iter 02192 | loss 1.3888 | loss(rot) 0.5281 | loss(pos) 0.4148 | loss(seq) 0.4459 | grad 3.2065 | lr 0.0010 | time_forward 3.0720 | time_backward 4.2610 |
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