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[2023-10-23 04:23:09,342::train::INFO] [train] Iter 566186 | loss 1.0771 | loss(rot) 0.7841 | loss(pos) 0.0350 | loss(seq) 0.2580 | grad 5.3340 | lr 0.0000 | time_forward 3.1160 | time_backward 4.0840 |
[2023-10-23 04:23:16,195::train::INFO] [train] Iter 566187 | loss 0.3570 | loss(rot) 0.3040 | loss(pos) 0.0414 | loss(seq) 0.0115 | grad 2.6510 | lr 0.0000 | time_forward 2.9830 | time_backward 3.8660 |
[2023-10-23 04:23:22,951::train::INFO] [train] Iter 566188 | loss 0.6466 | loss(rot) 0.5104 | loss(pos) 0.0628 | loss(seq) 0.0734 | grad 2.2188 | lr 0.0000 | time_forward 2.9610 | time_backward 3.7920 |
[2023-10-23 04:23:25,573::train::INFO] [train] Iter 566189 | loss 0.4746 | loss(rot) 0.4092 | loss(pos) 0.0373 | loss(seq) 0.0281 | grad 3.2329 | lr 0.0000 | time_forward 1.2410 | time_backward 1.3780 |
[2023-10-23 04:23:32,618::train::INFO] [train] Iter 566190 | loss 0.7532 | loss(rot) 0.4150 | loss(pos) 0.0429 | loss(seq) 0.2953 | grad 4.1967 | lr 0.0000 | time_forward 3.0480 | time_backward 3.9940 |
[2023-10-23 04:23:36,691::train::INFO] [train] Iter 566191 | loss 2.2120 | loss(rot) 1.9526 | loss(pos) 0.0690 | loss(seq) 0.1903 | grad 5.0556 | lr 0.0000 | time_forward 1.8560 | time_backward 2.2140 |
[2023-10-23 04:23:43,468::train::INFO] [train] Iter 566192 | loss 4.2158 | loss(rot) 0.0060 | loss(pos) 4.2098 | loss(seq) 0.0000 | grad 43.4394 | lr 0.0000 | time_forward 2.8770 | time_backward 3.8980 |
[2023-10-23 04:23:46,132::train::INFO] [train] Iter 566193 | loss 0.1134 | loss(rot) 0.0390 | loss(pos) 0.0523 | loss(seq) 0.0220 | grad 1.8881 | lr 0.0000 | time_forward 1.2440 | time_backward 1.3760 |
[2023-10-23 04:23:52,865::train::INFO] [train] Iter 566194 | loss 1.3691 | loss(rot) 0.9357 | loss(pos) 0.0292 | loss(seq) 0.4042 | grad 3.2114 | lr 0.0000 | time_forward 2.9020 | time_backward 3.8230 |
[2023-10-23 04:23:55,565::train::INFO] [train] Iter 566195 | loss 0.1820 | loss(rot) 0.1200 | loss(pos) 0.0574 | loss(seq) 0.0046 | grad 3.3986 | lr 0.0000 | time_forward 1.2440 | time_backward 1.4520 |
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