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[2023-09-02 04:56:25,315::train::INFO] [train] Iter 06179 | loss 1.9936 | loss(rot) 1.5166 | loss(pos) 0.1686 | loss(seq) 0.3083 | grad 3.7147 | lr 0.0010 | time_forward 3.9500 | time_backward 5.8520 |
[2023-09-02 04:56:33,595::train::INFO] [train] Iter 06180 | loss 2.5080 | loss(rot) 1.9759 | loss(pos) 0.0787 | loss(seq) 0.4533 | grad 4.2950 | lr 0.0010 | time_forward 3.5230 | time_backward 4.7540 |
[2023-09-02 04:56:42,078::train::INFO] [train] Iter 06181 | loss 1.9494 | loss(rot) 1.2735 | loss(pos) 0.2268 | loss(seq) 0.4490 | grad 4.1227 | lr 0.0010 | time_forward 3.4740 | time_backward 5.0060 |
[2023-09-02 04:56:50,802::train::INFO] [train] Iter 06182 | loss 0.8204 | loss(rot) 0.1733 | loss(pos) 0.6100 | loss(seq) 0.0370 | grad 4.2215 | lr 0.0010 | time_forward 3.5740 | time_backward 5.1470 |
[2023-09-02 04:56:58,942::train::INFO] [train] Iter 06183 | loss 2.3077 | loss(rot) 2.2489 | loss(pos) 0.0499 | loss(seq) 0.0089 | grad 4.8929 | lr 0.0010 | time_forward 3.3190 | time_backward 4.8190 |
[2023-09-02 04:57:01,930::train::INFO] [train] Iter 06184 | loss 2.1131 | loss(rot) 1.9877 | loss(pos) 0.1157 | loss(seq) 0.0097 | grad 5.5486 | lr 0.0010 | time_forward 1.4650 | time_backward 1.5190 |
[2023-09-02 04:57:09,889::train::INFO] [train] Iter 06185 | loss 0.5002 | loss(rot) 0.0245 | loss(pos) 0.4718 | loss(seq) 0.0040 | grad 5.3352 | lr 0.0010 | time_forward 3.2460 | time_backward 4.6910 |
[2023-09-02 04:57:18,380::train::INFO] [train] Iter 06186 | loss 3.0352 | loss(rot) 2.9074 | loss(pos) 0.1209 | loss(seq) 0.0069 | grad 3.5916 | lr 0.0010 | time_forward 3.5950 | time_backward 4.8920 |
[2023-09-02 04:57:28,431::train::INFO] [train] Iter 06187 | loss 1.7614 | loss(rot) 1.2632 | loss(pos) 0.1386 | loss(seq) 0.3595 | grad 4.3046 | lr 0.0010 | time_forward 4.1600 | time_backward 5.8890 |
[2023-09-02 04:57:31,635::train::INFO] [train] Iter 06188 | loss 0.9089 | loss(rot) 0.2822 | loss(pos) 0.5793 | loss(seq) 0.0474 | grad 5.2505 | lr 0.0010 | time_forward 1.7980 | time_backward 1.4020 |
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