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[2023-10-25 11:35:25,132::train::INFO] [train] Iter 595358 | loss 1.3995 | loss(rot) 1.3654 | loss(pos) 0.0221 | loss(seq) 0.0121 | grad 4.0690 | lr 0.0000 | time_forward 1.5680 | time_backward 1.9060 |
[2023-10-25 11:35:32,586::train::INFO] [train] Iter 595359 | loss 1.4149 | loss(rot) 1.4011 | loss(pos) 0.0115 | loss(seq) 0.0022 | grad 4.3530 | lr 0.0000 | time_forward 3.1110 | time_backward 4.3280 |
[2023-10-25 11:35:40,166::train::INFO] [train] Iter 595360 | loss 0.2620 | loss(rot) 0.2188 | loss(pos) 0.0172 | loss(seq) 0.0259 | grad 2.5782 | lr 0.0000 | time_forward 3.3520 | time_backward 4.2250 |
[2023-10-25 11:35:50,109::train::INFO] [train] Iter 595361 | loss 0.7254 | loss(rot) 0.3744 | loss(pos) 0.1225 | loss(seq) 0.2285 | grad 2.9597 | lr 0.0000 | time_forward 4.1730 | time_backward 5.7670 |
[2023-10-25 11:35:58,196::train::INFO] [train] Iter 595362 | loss 0.3944 | loss(rot) 0.1715 | loss(pos) 0.1910 | loss(seq) 0.0319 | grad 3.7468 | lr 0.0000 | time_forward 3.3820 | time_backward 4.7010 |
[2023-10-25 11:36:08,376::train::INFO] [train] Iter 595363 | loss 1.0305 | loss(rot) 0.3457 | loss(pos) 0.0524 | loss(seq) 0.6324 | grad 3.7300 | lr 0.0000 | time_forward 4.2040 | time_backward 5.9720 |
[2023-10-25 11:36:11,264::train::INFO] [train] Iter 595364 | loss 0.5079 | loss(rot) 0.0484 | loss(pos) 0.4487 | loss(seq) 0.0108 | grad 4.3496 | lr 0.0000 | time_forward 1.4350 | time_backward 1.4500 |
[2023-10-25 11:36:19,959::train::INFO] [train] Iter 595365 | loss 0.2718 | loss(rot) 0.2508 | loss(pos) 0.0210 | loss(seq) 0.0000 | grad 2.7859 | lr 0.0000 | time_forward 3.6430 | time_backward 5.0480 |
[2023-10-25 11:36:26,091::train::INFO] [train] Iter 595366 | loss 0.3474 | loss(rot) 0.1408 | loss(pos) 0.0573 | loss(seq) 0.1493 | grad 3.3815 | lr 0.0000 | time_forward 2.6550 | time_backward 3.4740 |
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