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[2023-10-23 04:50:59,855::train::INFO] [train] Iter 566486 | loss 0.7213 | loss(rot) 0.2420 | loss(pos) 0.2846 | loss(seq) 0.1947 | grad 7.1185 | lr 0.0000 | time_forward 1.2620 | time_backward 1.4680 |
[2023-10-23 04:51:07,835::train::INFO] [train] Iter 566487 | loss 1.6588 | loss(rot) 1.2620 | loss(pos) 0.1000 | loss(seq) 0.2968 | grad 7.9692 | lr 0.0000 | time_forward 3.3280 | time_backward 4.6480 |
[2023-10-23 04:51:15,259::train::INFO] [train] Iter 566488 | loss 0.7726 | loss(rot) 0.1554 | loss(pos) 0.6123 | loss(seq) 0.0049 | grad 6.8461 | lr 0.0000 | time_forward 3.2340 | time_backward 4.1870 |
[2023-10-23 04:51:18,394::train::INFO] [train] Iter 566489 | loss 0.9775 | loss(rot) 0.1217 | loss(pos) 0.8436 | loss(seq) 0.0122 | grad 5.3231 | lr 0.0000 | time_forward 1.4190 | time_backward 1.7130 |
[2023-10-23 04:51:26,433::train::INFO] [train] Iter 566490 | loss 1.3930 | loss(rot) 0.8465 | loss(pos) 0.0811 | loss(seq) 0.4655 | grad 3.6557 | lr 0.0000 | time_forward 3.3180 | time_backward 4.7090 |
[2023-10-23 04:51:33,902::train::INFO] [train] Iter 566491 | loss 1.2116 | loss(rot) 1.1719 | loss(pos) 0.0353 | loss(seq) 0.0045 | grad 7.6091 | lr 0.0000 | time_forward 3.2320 | time_backward 4.2330 |
[2023-10-23 04:51:42,063::train::INFO] [train] Iter 566492 | loss 0.9378 | loss(rot) 0.0361 | loss(pos) 0.8999 | loss(seq) 0.0017 | grad 8.8859 | lr 0.0000 | time_forward 3.3370 | time_backward 4.8210 |
[2023-10-23 04:51:49,063::train::INFO] [train] Iter 566493 | loss 1.8756 | loss(rot) 1.5937 | loss(pos) 0.0541 | loss(seq) 0.2277 | grad 4.1169 | lr 0.0000 | time_forward 3.0640 | time_backward 3.9320 |
[2023-10-23 04:51:54,726::train::INFO] [train] Iter 566494 | loss 1.0138 | loss(rot) 0.5472 | loss(pos) 0.3073 | loss(seq) 0.1592 | grad 6.2932 | lr 0.0000 | time_forward 2.4840 | time_backward 3.1760 |
[2023-10-23 04:52:01,561::train::INFO] [train] Iter 566495 | loss 1.1610 | loss(rot) 0.3612 | loss(pos) 0.4866 | loss(seq) 0.3132 | grad 5.0595 | lr 0.0000 | time_forward 2.9360 | time_backward 3.8960 |
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