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[2023-09-01 22:49:22,493::train::INFO] [train] Iter 03182 | loss 2.8113 | loss(rot) 0.8182 | loss(pos) 1.6962 | loss(seq) 0.2969 | grad 6.4743 | lr 0.0010 | time_forward 4.2300 | time_backward 5.8680 |
[2023-09-01 22:49:32,353::train::INFO] [train] Iter 03183 | loss 3.3258 | loss(rot) 2.8261 | loss(pos) 0.3711 | loss(seq) 0.1287 | grad 6.0080 | lr 0.0010 | time_forward 4.1250 | time_backward 5.7310 |
[2023-09-01 22:49:35,624::train::INFO] [train] Iter 03184 | loss 3.1682 | loss(rot) 2.3516 | loss(pos) 0.5737 | loss(seq) 0.2429 | grad 4.3287 | lr 0.0010 | time_forward 1.5870 | time_backward 1.6800 |
[2023-09-01 22:49:38,377::train::INFO] [train] Iter 03185 | loss 2.4446 | loss(rot) 1.6036 | loss(pos) 0.3285 | loss(seq) 0.5125 | grad 3.2152 | lr 0.0010 | time_forward 1.3180 | time_backward 1.4310 |
[2023-09-01 22:49:48,543::train::INFO] [train] Iter 03186 | loss 2.5968 | loss(rot) 2.3952 | loss(pos) 0.1918 | loss(seq) 0.0098 | grad 5.6719 | lr 0.0010 | time_forward 4.1670 | time_backward 5.9960 |
[2023-09-01 22:49:51,271::train::INFO] [train] Iter 03187 | loss 2.5421 | loss(rot) 1.9270 | loss(pos) 0.2408 | loss(seq) 0.3743 | grad 4.1334 | lr 0.0010 | time_forward 1.2840 | time_backward 1.4410 |
[2023-09-01 22:50:00,012::train::INFO] [train] Iter 03188 | loss 1.1008 | loss(rot) 0.4339 | loss(pos) 0.5925 | loss(seq) 0.0744 | grad 4.9687 | lr 0.0010 | time_forward 3.8240 | time_backward 4.9130 |
[2023-09-01 22:50:06,629::train::INFO] [train] Iter 03189 | loss 1.5761 | loss(rot) 0.8503 | loss(pos) 0.6120 | loss(seq) 0.1137 | grad 3.0382 | lr 0.0010 | time_forward 2.7710 | time_backward 3.8430 |
[2023-09-01 22:50:13,381::train::INFO] [train] Iter 03190 | loss 3.3482 | loss(rot) 3.0425 | loss(pos) 0.2056 | loss(seq) 0.1000 | grad 4.2307 | lr 0.0010 | time_forward 2.8490 | time_backward 3.8990 |
[2023-09-01 22:50:22,423::train::INFO] [train] Iter 03191 | loss 1.7694 | loss(rot) 0.1810 | loss(pos) 1.5669 | loss(seq) 0.0215 | grad 6.5694 | lr 0.0010 | time_forward 4.7840 | time_backward 4.2540 |
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