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[2023-09-02 16:32:33,221::train::INFO] [train] Iter 11974 | loss 2.8387 | loss(rot) 1.7751 | loss(pos) 0.4054 | loss(seq) 0.6583 | grad 3.3970 | lr 0.0010 | time_forward 2.7770 | time_backward 4.4320 |
[2023-09-02 16:32:42,258::train::INFO] [train] Iter 11975 | loss 1.4249 | loss(rot) 0.6986 | loss(pos) 0.4562 | loss(seq) 0.2702 | grad 2.9134 | lr 0.0010 | time_forward 3.7140 | time_backward 5.3200 |
[2023-09-02 16:32:51,916::train::INFO] [train] Iter 11976 | loss 2.1130 | loss(rot) 1.8975 | loss(pos) 0.1857 | loss(seq) 0.0298 | grad 4.8172 | lr 0.0010 | time_forward 3.8720 | time_backward 5.7840 |
[2023-09-02 16:33:00,341::train::INFO] [train] Iter 11977 | loss 0.7277 | loss(rot) 0.5444 | loss(pos) 0.1325 | loss(seq) 0.0508 | grad 3.8849 | lr 0.0010 | time_forward 3.6260 | time_backward 4.7960 |
[2023-09-02 16:33:08,735::train::INFO] [train] Iter 11978 | loss 2.0974 | loss(rot) 1.9176 | loss(pos) 0.1546 | loss(seq) 0.0252 | grad 4.8851 | lr 0.0010 | time_forward 3.6550 | time_backward 4.7350 |
[2023-09-02 16:33:17,656::train::INFO] [train] Iter 11979 | loss 2.1298 | loss(rot) 2.0045 | loss(pos) 0.1232 | loss(seq) 0.0021 | grad 4.5299 | lr 0.0010 | time_forward 3.3230 | time_backward 5.5950 |
[2023-09-02 16:33:25,188::train::INFO] [train] Iter 11980 | loss 1.6398 | loss(rot) 0.9367 | loss(pos) 0.3928 | loss(seq) 0.3103 | grad 4.7672 | lr 0.0010 | time_forward 3.0850 | time_backward 4.4430 |
[2023-09-02 16:33:27,538::train::INFO] [train] Iter 11981 | loss 0.7931 | loss(rot) 0.7193 | loss(pos) 0.0642 | loss(seq) 0.0096 | grad 3.4104 | lr 0.0010 | time_forward 1.1550 | time_backward 1.1910 |
[2023-09-02 16:33:36,394::train::INFO] [train] Iter 11982 | loss 1.5048 | loss(rot) 0.6069 | loss(pos) 0.3399 | loss(seq) 0.5580 | grad 3.8914 | lr 0.0010 | time_forward 3.4240 | time_backward 5.4290 |
[2023-09-02 16:33:44,640::train::INFO] [train] Iter 11983 | loss 2.4186 | loss(rot) 2.0282 | loss(pos) 0.1638 | loss(seq) 0.2266 | grad 4.0123 | lr 0.0010 | time_forward 3.2740 | time_backward 4.9680 |
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