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[2023-09-01 20:35:31,522::train::INFO] [train] Iter 02083 | loss 1.6953 | loss(rot) 0.8898 | loss(pos) 0.3483 | loss(seq) 0.4572 | grad 3.7374 | lr 0.0010 | time_forward 3.7780 | time_backward 5.2750 |
[2023-09-01 20:35:39,009::train::INFO] [train] Iter 02084 | loss 3.1456 | loss(rot) 0.0072 | loss(pos) 3.1384 | loss(seq) 0.0000 | grad 4.4613 | lr 0.0010 | time_forward 3.1600 | time_backward 4.3230 |
[2023-09-01 20:35:47,335::train::INFO] [train] Iter 02085 | loss 2.8355 | loss(rot) 2.3562 | loss(pos) 0.1728 | loss(seq) 0.3064 | grad 3.1047 | lr 0.0010 | time_forward 3.4810 | time_backward 4.8430 |
[2023-09-01 20:35:57,686::train::INFO] [train] Iter 02086 | loss 1.7202 | loss(rot) 0.7489 | loss(pos) 0.5430 | loss(seq) 0.4283 | grad 4.0633 | lr 0.0010 | time_forward 4.2990 | time_backward 6.0480 |
[2023-09-01 20:36:00,391::train::INFO] [train] Iter 02087 | loss 2.8210 | loss(rot) 1.2845 | loss(pos) 1.4504 | loss(seq) 0.0861 | grad 5.9937 | lr 0.0010 | time_forward 1.2450 | time_backward 1.4580 |
[2023-09-01 20:36:03,158::train::INFO] [train] Iter 02088 | loss 2.9080 | loss(rot) 2.5405 | loss(pos) 0.3045 | loss(seq) 0.0631 | grad 3.6986 | lr 0.0010 | time_forward 1.2860 | time_backward 1.4770 |
[2023-09-01 20:36:05,863::train::INFO] [train] Iter 02089 | loss 2.2170 | loss(rot) 1.9185 | loss(pos) 0.1089 | loss(seq) 0.1895 | grad 2.8290 | lr 0.0010 | time_forward 1.2890 | time_backward 1.4130 |
[2023-09-01 20:36:08,134::train::INFO] [train] Iter 02090 | loss 3.3531 | loss(rot) 3.2203 | loss(pos) 0.1327 | loss(seq) 0.0001 | grad 1.5351 | lr 0.0010 | time_forward 1.1180 | time_backward 1.1490 |
[2023-09-01 20:36:10,787::train::INFO] [train] Iter 02091 | loss 2.7801 | loss(rot) 0.8797 | loss(pos) 1.8821 | loss(seq) 0.0183 | grad 4.9772 | lr 0.0010 | time_forward 1.2320 | time_backward 1.4060 |
[2023-09-01 20:36:19,953::train::INFO] [train] Iter 02092 | loss 2.6277 | loss(rot) 2.1724 | loss(pos) 0.3380 | loss(seq) 0.1172 | grad 3.0927 | lr 0.0010 | time_forward 4.2520 | time_backward 4.8870 |
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