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[2023-10-22 18:42:40,845::train::INFO] [train] Iter 560692 | loss 0.6189 | loss(rot) 0.5911 | loss(pos) 0.0277 | loss(seq) 0.0001 | grad 6.0771 | lr 0.0000 | time_forward 3.7040 | time_backward 5.1870 |
[2023-10-22 18:42:50,930::train::INFO] [train] Iter 560693 | loss 0.3997 | loss(rot) 0.2256 | loss(pos) 0.0311 | loss(seq) 0.1430 | grad 2.5940 | lr 0.0000 | time_forward 4.2600 | time_backward 5.8220 |
[2023-10-22 18:43:01,180::train::INFO] [train] Iter 560694 | loss 2.0659 | loss(rot) 1.9648 | loss(pos) 0.0898 | loss(seq) 0.0113 | grad 4.9852 | lr 0.0000 | time_forward 4.3550 | time_backward 5.8910 |
[2023-10-22 18:43:08,595::train::INFO] [train] Iter 560695 | loss 0.2990 | loss(rot) 0.1822 | loss(pos) 0.0187 | loss(seq) 0.0981 | grad 2.2212 | lr 0.0000 | time_forward 3.0860 | time_backward 4.3260 |
[2023-10-22 18:43:11,337::train::INFO] [train] Iter 560696 | loss 0.8681 | loss(rot) 0.8107 | loss(pos) 0.0237 | loss(seq) 0.0336 | grad 2.8210 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4290 |
[2023-10-22 18:43:18,810::train::INFO] [train] Iter 560697 | loss 0.4922 | loss(rot) 0.3590 | loss(pos) 0.0218 | loss(seq) 0.1115 | grad 2.0093 | lr 0.0000 | time_forward 3.1770 | time_backward 4.2620 |
[2023-10-22 18:43:28,706::train::INFO] [train] Iter 560698 | loss 0.4945 | loss(rot) 0.1367 | loss(pos) 0.0547 | loss(seq) 0.3031 | grad 5.0223 | lr 0.0000 | time_forward 3.7010 | time_backward 6.1910 |
[2023-10-22 18:43:31,280::train::INFO] [train] Iter 560699 | loss 0.9163 | loss(rot) 0.0926 | loss(pos) 0.8212 | loss(seq) 0.0026 | grad 8.4391 | lr 0.0000 | time_forward 1.2420 | time_backward 1.3280 |
[2023-10-22 18:43:40,894::train::INFO] [train] Iter 560700 | loss 1.5921 | loss(rot) 0.0101 | loss(pos) 1.5819 | loss(seq) 0.0001 | grad 12.3511 | lr 0.0000 | time_forward 4.0560 | time_backward 5.5400 |
[2023-10-22 18:43:49,834::train::INFO] [train] Iter 560701 | loss 0.4264 | loss(rot) 0.2983 | loss(pos) 0.0204 | loss(seq) 0.1077 | grad 2.7238 | lr 0.0000 | time_forward 3.8470 | time_backward 5.0900 |
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