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[2023-10-25 00:56:15,580::train::INFO] [train] Iter 589663 | loss 0.3958 | loss(rot) 0.0979 | loss(pos) 0.0304 | loss(seq) 0.2675 | grad 2.8508 | lr 0.0000 | time_forward 1.2400 | time_backward 1.4580 |
[2023-10-25 00:56:22,801::train::INFO] [train] Iter 589664 | loss 0.4192 | loss(rot) 0.3438 | loss(pos) 0.0754 | loss(seq) 0.0000 | grad 5.3170 | lr 0.0000 | time_forward 3.0380 | time_backward 4.1800 |
[2023-10-25 00:56:31,655::train::INFO] [train] Iter 589665 | loss 0.3582 | loss(rot) 0.1292 | loss(pos) 0.0624 | loss(seq) 0.1666 | grad 2.5640 | lr 0.0000 | time_forward 3.6410 | time_backward 5.2100 |
[2023-10-25 00:56:34,146::train::INFO] [train] Iter 589666 | loss 1.0033 | loss(rot) 0.7273 | loss(pos) 0.0722 | loss(seq) 0.2037 | grad 4.2073 | lr 0.0000 | time_forward 1.2080 | time_backward 1.2810 |
[2023-10-25 00:56:41,279::train::INFO] [train] Iter 589667 | loss 0.4214 | loss(rot) 0.1503 | loss(pos) 0.2591 | loss(seq) 0.0119 | grad 3.9879 | lr 0.0000 | time_forward 3.0150 | time_backward 4.0900 |
[2023-10-25 00:56:50,011::train::INFO] [train] Iter 589668 | loss 0.8346 | loss(rot) 0.2582 | loss(pos) 0.2994 | loss(seq) 0.2771 | grad 4.0578 | lr 0.0000 | time_forward 3.5670 | time_backward 5.1620 |
[2023-10-25 00:56:57,734::train::INFO] [train] Iter 589669 | loss 2.2617 | loss(rot) 2.1965 | loss(pos) 0.0651 | loss(seq) 0.0000 | grad 6.3294 | lr 0.0000 | time_forward 3.2660 | time_backward 4.4540 |
[2023-10-25 00:57:05,759::train::INFO] [train] Iter 589670 | loss 0.3137 | loss(rot) 0.0165 | loss(pos) 0.2923 | loss(seq) 0.0049 | grad 5.3366 | lr 0.0000 | time_forward 3.4200 | time_backward 4.6030 |
[2023-10-25 00:57:12,420::train::INFO] [train] Iter 589671 | loss 0.4301 | loss(rot) 0.1826 | loss(pos) 0.0231 | loss(seq) 0.2245 | grad 2.3921 | lr 0.0000 | time_forward 2.8620 | time_backward 3.7950 |
[2023-10-25 00:57:20,076::train::INFO] [train] Iter 589672 | loss 1.5079 | loss(rot) 1.4760 | loss(pos) 0.0310 | loss(seq) 0.0009 | grad 5.0223 | lr 0.0000 | time_forward 3.2660 | time_backward 4.3880 |
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