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[2023-10-24 22:17:07,431::train::INFO] [train] Iter 588364 | loss 1.4269 | loss(rot) 0.6916 | loss(pos) 0.3101 | loss(seq) 0.4252 | grad 3.3653 | lr 0.0000 | time_forward 4.1250 | time_backward 5.9930 |
[2023-10-24 22:17:10,216::train::INFO] [train] Iter 588365 | loss 0.6502 | loss(rot) 0.1971 | loss(pos) 0.2316 | loss(seq) 0.2215 | grad 4.4090 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4680 |
[2023-10-24 22:17:13,076::train::INFO] [train] Iter 588366 | loss 0.4228 | loss(rot) 0.1328 | loss(pos) 0.0788 | loss(seq) 0.2112 | grad 3.1064 | lr 0.0000 | time_forward 1.3930 | time_backward 1.4650 |
[2023-10-24 22:17:15,631::train::INFO] [train] Iter 588367 | loss 2.0689 | loss(rot) 1.7381 | loss(pos) 0.0786 | loss(seq) 0.2522 | grad 3.9414 | lr 0.0000 | time_forward 1.2450 | time_backward 1.3070 |
[2023-10-24 22:17:24,237::train::INFO] [train] Iter 588368 | loss 4.0741 | loss(rot) 0.0036 | loss(pos) 4.0705 | loss(seq) 0.0000 | grad 25.9220 | lr 0.0000 | time_forward 3.6590 | time_backward 4.9440 |
[2023-10-24 22:17:26,907::train::INFO] [train] Iter 588369 | loss 0.5593 | loss(rot) 0.1949 | loss(pos) 0.1148 | loss(seq) 0.2496 | grad 3.7190 | lr 0.0000 | time_forward 1.2530 | time_backward 1.4130 |
[2023-10-24 22:17:30,401::train::INFO] [train] Iter 588370 | loss 0.6115 | loss(rot) 0.2697 | loss(pos) 0.0423 | loss(seq) 0.2995 | grad 3.1113 | lr 0.0000 | time_forward 1.5150 | time_backward 1.9420 |
[2023-10-24 22:17:38,842::train::INFO] [train] Iter 588371 | loss 0.6119 | loss(rot) 0.5397 | loss(pos) 0.0236 | loss(seq) 0.0486 | grad 23.5183 | lr 0.0000 | time_forward 3.5650 | time_backward 4.8730 |
[2023-10-24 22:17:47,173::train::INFO] [train] Iter 588372 | loss 0.1633 | loss(rot) 0.1383 | loss(pos) 0.0191 | loss(seq) 0.0059 | grad 3.0400 | lr 0.0000 | time_forward 3.4880 | time_backward 4.8280 |
[2023-10-24 22:17:49,958::train::INFO] [train] Iter 588373 | loss 0.3710 | loss(rot) 0.3430 | loss(pos) 0.0204 | loss(seq) 0.0076 | grad 5.2363 | lr 0.0000 | time_forward 1.3300 | time_backward 1.4520 |
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