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[2023-09-02 07:56:52,120::train::INFO] [train] Iter 07678 | loss 1.2120 | loss(rot) 0.6832 | loss(pos) 0.2821 | loss(seq) 0.2468 | grad 4.3280 | lr 0.0010 | time_forward 1.2720 | time_backward 1.4140 |
[2023-09-02 07:57:02,384::train::INFO] [train] Iter 07679 | loss 1.4195 | loss(rot) 0.5561 | loss(pos) 0.3227 | loss(seq) 0.5407 | grad 2.7324 | lr 0.0010 | time_forward 4.1360 | time_backward 6.1250 |
[2023-09-02 07:57:09,635::train::INFO] [train] Iter 07680 | loss 1.0512 | loss(rot) 0.4901 | loss(pos) 0.2126 | loss(seq) 0.3485 | grad 3.6820 | lr 0.0010 | time_forward 3.0460 | time_backward 4.2020 |
[2023-09-02 07:57:12,328::train::INFO] [train] Iter 07681 | loss 0.8242 | loss(rot) 0.0765 | loss(pos) 0.7264 | loss(seq) 0.0213 | grad 5.6012 | lr 0.0010 | time_forward 1.2520 | time_backward 1.4380 |
[2023-09-02 07:57:23,669::train::INFO] [train] Iter 07682 | loss 1.9227 | loss(rot) 0.5394 | loss(pos) 1.0973 | loss(seq) 0.2860 | grad 5.6266 | lr 0.0010 | time_forward 5.3800 | time_backward 5.9570 |
[2023-09-02 07:57:33,738::train::INFO] [train] Iter 07683 | loss 1.9263 | loss(rot) 1.1670 | loss(pos) 0.2385 | loss(seq) 0.5208 | grad 4.5014 | lr 0.0010 | time_forward 4.2670 | time_backward 5.7990 |
[2023-09-02 07:57:40,806::train::INFO] [train] Iter 07684 | loss 1.0130 | loss(rot) 0.3035 | loss(pos) 0.2082 | loss(seq) 0.5013 | grad 5.2468 | lr 0.0010 | time_forward 3.0030 | time_backward 4.0610 |
[2023-09-02 07:57:43,295::train::INFO] [train] Iter 07685 | loss 2.9588 | loss(rot) 2.8396 | loss(pos) 0.0994 | loss(seq) 0.0198 | grad 3.9072 | lr 0.0010 | time_forward 1.2130 | time_backward 1.2730 |
[2023-09-02 07:57:51,600::train::INFO] [train] Iter 07686 | loss 2.3203 | loss(rot) 0.0355 | loss(pos) 2.2841 | loss(seq) 0.0007 | grad 6.2983 | lr 0.0010 | time_forward 3.5150 | time_backward 4.7860 |
[2023-09-02 07:57:54,333::train::INFO] [train] Iter 07687 | loss 1.5593 | loss(rot) 0.6372 | loss(pos) 0.4543 | loss(seq) 0.4678 | grad 3.9530 | lr 0.0010 | time_forward 1.2890 | time_backward 1.4400 |
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