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[2023-10-24 17:40:44,370::train::INFO] [train] Iter 585967 | loss 1.0027 | loss(rot) 0.1932 | loss(pos) 0.7733 | loss(seq) 0.0361 | grad 5.1887 | lr 0.0000 | time_forward 1.2430 | time_backward 1.3740 |
[2023-10-24 17:40:51,215::train::INFO] [train] Iter 585968 | loss 1.7141 | loss(rot) 1.6705 | loss(pos) 0.0290 | loss(seq) 0.0147 | grad 8.2673 | lr 0.0000 | time_forward 2.9710 | time_backward 3.8500 |
[2023-10-24 17:40:56,686::train::INFO] [train] Iter 585969 | loss 1.3026 | loss(rot) 1.1489 | loss(pos) 0.0330 | loss(seq) 0.1207 | grad 34.2199 | lr 0.0000 | time_forward 2.3930 | time_backward 3.0750 |
[2023-10-24 17:41:02,851::train::INFO] [train] Iter 585970 | loss 0.5913 | loss(rot) 0.1469 | loss(pos) 0.1996 | loss(seq) 0.2448 | grad 3.4063 | lr 0.0000 | time_forward 2.6590 | time_backward 3.5030 |
[2023-10-24 17:41:09,776::train::INFO] [train] Iter 585971 | loss 0.6816 | loss(rot) 0.5169 | loss(pos) 0.0187 | loss(seq) 0.1461 | grad 2.5898 | lr 0.0000 | time_forward 3.0100 | time_backward 3.9120 |
[2023-10-24 17:41:12,170::train::INFO] [train] Iter 585972 | loss 0.9766 | loss(rot) 0.8037 | loss(pos) 0.1088 | loss(seq) 0.0640 | grad 6.9553 | lr 0.0000 | time_forward 1.1560 | time_backward 1.2350 |
[2023-10-24 17:41:19,161::train::INFO] [train] Iter 585973 | loss 2.0846 | loss(rot) 1.7604 | loss(pos) 0.1047 | loss(seq) 0.2194 | grad 5.8213 | lr 0.0000 | time_forward 2.9870 | time_backward 4.0020 |
[2023-10-24 17:41:21,811::train::INFO] [train] Iter 585974 | loss 1.0087 | loss(rot) 0.2355 | loss(pos) 0.7671 | loss(seq) 0.0062 | grad 8.1216 | lr 0.0000 | time_forward 1.2740 | time_backward 1.3720 |
[2023-10-24 17:41:24,475::train::INFO] [train] Iter 585975 | loss 0.2837 | loss(rot) 0.0357 | loss(pos) 0.2368 | loss(seq) 0.0113 | grad 7.4650 | lr 0.0000 | time_forward 1.2840 | time_backward 1.3760 |
[2023-10-24 17:41:31,639::train::INFO] [train] Iter 585976 | loss 1.1449 | loss(rot) 0.9350 | loss(pos) 0.0568 | loss(seq) 0.1531 | grad 3.0941 | lr 0.0000 | time_forward 3.1320 | time_backward 4.0290 |
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