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[2023-10-24 10:31:38,729::train::INFO] [train] Iter 582170 | loss 1.6779 | loss(rot) 1.0664 | loss(pos) 0.1756 | loss(seq) 0.4360 | grad 4.4812 | lr 0.0000 | time_forward 4.5670 | time_backward 5.9290 |
[2023-10-24 10:31:41,286::train::INFO] [train] Iter 582171 | loss 0.5707 | loss(rot) 0.4518 | loss(pos) 0.0212 | loss(seq) 0.0977 | grad 2.3158 | lr 0.0000 | time_forward 1.2280 | time_backward 1.3250 |
[2023-10-24 10:31:48,633::train::INFO] [train] Iter 582172 | loss 1.1498 | loss(rot) 0.7576 | loss(pos) 0.0575 | loss(seq) 0.3347 | grad 14.9884 | lr 0.0000 | time_forward 3.2350 | time_backward 4.1080 |
[2023-10-24 10:31:58,733::train::INFO] [train] Iter 582173 | loss 1.2554 | loss(rot) 0.5374 | loss(pos) 0.3103 | loss(seq) 0.4077 | grad 3.4766 | lr 0.0000 | time_forward 4.1930 | time_backward 5.9040 |
[2023-10-24 10:32:08,751::train::INFO] [train] Iter 582174 | loss 1.0482 | loss(rot) 0.7591 | loss(pos) 0.0337 | loss(seq) 0.2554 | grad 4.7301 | lr 0.0000 | time_forward 4.0350 | time_backward 5.9800 |
[2023-10-24 10:32:18,854::train::INFO] [train] Iter 582175 | loss 0.6034 | loss(rot) 0.3027 | loss(pos) 0.0626 | loss(seq) 0.2381 | grad 28.8930 | lr 0.0000 | time_forward 4.2570 | time_backward 5.8430 |
[2023-10-24 10:32:28,827::train::INFO] [train] Iter 582176 | loss 0.2624 | loss(rot) 0.1571 | loss(pos) 0.0409 | loss(seq) 0.0644 | grad 3.9104 | lr 0.0000 | time_forward 4.0930 | time_backward 5.8760 |
[2023-10-24 10:32:31,760::train::INFO] [train] Iter 582177 | loss 0.7722 | loss(rot) 0.7344 | loss(pos) 0.0218 | loss(seq) 0.0160 | grad 17.9096 | lr 0.0000 | time_forward 1.3340 | time_backward 1.5970 |
[2023-10-24 10:32:41,862::train::INFO] [train] Iter 582178 | loss 0.6678 | loss(rot) 0.0403 | loss(pos) 0.6262 | loss(seq) 0.0013 | grad 9.2311 | lr 0.0000 | time_forward 4.0460 | time_backward 6.0340 |
[2023-10-24 10:32:50,441::train::INFO] [train] Iter 582179 | loss 0.2925 | loss(rot) 0.1539 | loss(pos) 0.0855 | loss(seq) 0.0530 | grad 3.2056 | lr 0.0000 | time_forward 3.5610 | time_backward 5.0150 |
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