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[2023-09-02 00:40:57,644::train::INFO] [train] Iter 04081 | loss 1.6670 | loss(rot) 0.2410 | loss(pos) 1.3574 | loss(seq) 0.0685 | grad 6.4169 | lr 0.0010 | time_forward 1.3280 | time_backward 1.5650 |
[2023-09-02 00:41:04,132::train::INFO] [train] Iter 04082 | loss 1.9642 | loss(rot) 1.1892 | loss(pos) 0.2523 | loss(seq) 0.5226 | grad 3.3839 | lr 0.0010 | time_forward 2.6350 | time_backward 3.8490 |
[2023-09-02 00:41:14,718::train::INFO] [train] Iter 04083 | loss 2.3846 | loss(rot) 1.9640 | loss(pos) 0.1457 | loss(seq) 0.2749 | grad 4.6287 | lr 0.0010 | time_forward 4.3030 | time_backward 6.2800 |
[2023-09-02 00:41:24,892::train::INFO] [train] Iter 04084 | loss 2.9093 | loss(rot) 2.4980 | loss(pos) 0.1971 | loss(seq) 0.2142 | grad 4.2976 | lr 0.0010 | time_forward 4.0780 | time_backward 6.0930 |
[2023-09-02 00:41:33,846::train::INFO] [train] Iter 04085 | loss 1.8778 | loss(rot) 1.1741 | loss(pos) 0.1945 | loss(seq) 0.5092 | grad 3.5261 | lr 0.0010 | time_forward 3.7220 | time_backward 5.2270 |
[2023-09-02 00:41:36,219::train::INFO] [train] Iter 04086 | loss 1.5653 | loss(rot) 0.9092 | loss(pos) 0.3545 | loss(seq) 0.3016 | grad 3.7792 | lr 0.0010 | time_forward 1.0860 | time_backward 1.2820 |
[2023-09-02 00:41:44,717::train::INFO] [train] Iter 04087 | loss 2.8382 | loss(rot) 2.5465 | loss(pos) 0.2875 | loss(seq) 0.0043 | grad 6.1712 | lr 0.0010 | time_forward 3.4900 | time_backward 5.0040 |
[2023-09-02 00:41:47,562::train::INFO] [train] Iter 04088 | loss 1.0436 | loss(rot) 0.0481 | loss(pos) 0.9894 | loss(seq) 0.0061 | grad 4.2790 | lr 0.0010 | time_forward 1.2900 | time_backward 1.5500 |
[2023-09-02 00:41:56,736::train::INFO] [train] Iter 04089 | loss 1.5338 | loss(rot) 1.3251 | loss(pos) 0.2081 | loss(seq) 0.0007 | grad 5.1255 | lr 0.0010 | time_forward 3.8150 | time_backward 5.3330 |
[2023-09-02 00:41:59,770::train::INFO] [train] Iter 04090 | loss 2.4915 | loss(rot) 2.2830 | loss(pos) 0.2084 | loss(seq) 0.0000 | grad 4.3341 | lr 0.0010 | time_forward 1.4820 | time_backward 1.5480 |
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