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[2023-09-02 09:22:06,749::train::INFO] [train] Iter 08377 | loss 4.4183 | loss(rot) 0.0217 | loss(pos) 4.3966 | loss(seq) 0.0000 | grad 10.2608 | lr 0.0010 | time_forward 3.6160 | time_backward 4.9870 |
[2023-09-02 09:22:15,811::train::INFO] [train] Iter 08378 | loss 2.0611 | loss(rot) 1.4829 | loss(pos) 0.1868 | loss(seq) 0.3914 | grad 4.7346 | lr 0.0010 | time_forward 3.8550 | time_backward 5.2030 |
[2023-09-02 09:22:24,084::train::INFO] [train] Iter 08379 | loss 0.6409 | loss(rot) 0.1226 | loss(pos) 0.4727 | loss(seq) 0.0456 | grad 5.3634 | lr 0.0010 | time_forward 3.4650 | time_backward 4.8040 |
[2023-09-02 09:22:26,776::train::INFO] [train] Iter 08380 | loss 0.5530 | loss(rot) 0.0919 | loss(pos) 0.4362 | loss(seq) 0.0249 | grad 3.9572 | lr 0.0010 | time_forward 1.2800 | time_backward 1.4080 |
[2023-09-02 09:22:36,806::train::INFO] [train] Iter 08381 | loss 1.1896 | loss(rot) 0.4177 | loss(pos) 0.4813 | loss(seq) 0.2907 | grad 3.8248 | lr 0.0010 | time_forward 4.0970 | time_backward 5.9120 |
[2023-09-02 09:22:47,414::train::INFO] [train] Iter 08382 | loss 1.6287 | loss(rot) 0.0837 | loss(pos) 1.5396 | loss(seq) 0.0054 | grad 5.8810 | lr 0.0010 | time_forward 4.1380 | time_backward 6.4670 |
[2023-09-02 09:23:00,432::train::INFO] [train] Iter 08383 | loss 1.2114 | loss(rot) 0.2115 | loss(pos) 0.5898 | loss(seq) 0.4101 | grad 4.0654 | lr 0.0010 | time_forward 4.5990 | time_backward 8.4150 |
[2023-09-02 09:23:03,282::train::INFO] [train] Iter 08384 | loss 2.2457 | loss(rot) 1.9559 | loss(pos) 0.1518 | loss(seq) 0.1380 | grad 5.8507 | lr 0.0010 | time_forward 1.3690 | time_backward 1.4770 |
[2023-09-02 09:23:12,257::train::INFO] [train] Iter 08385 | loss 1.9592 | loss(rot) 1.4404 | loss(pos) 0.1699 | loss(seq) 0.3489 | grad 5.0847 | lr 0.0010 | time_forward 3.5810 | time_backward 5.3900 |
[2023-09-02 09:23:15,000::train::INFO] [train] Iter 08386 | loss 2.3236 | loss(rot) 1.8272 | loss(pos) 0.2441 | loss(seq) 0.2523 | grad 4.1075 | lr 0.0010 | time_forward 1.2480 | time_backward 1.4920 |
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