text
stringlengths 56
1.16k
|
|---|
[2023-09-02 14:56:28,433::train::INFO] [train] Iter 11084 | loss 1.3923 | loss(rot) 0.9633 | loss(pos) 0.2142 | loss(seq) 0.2148 | grad 6.1624 | lr 0.0010 | time_forward 3.4190 | time_backward 5.4140 |
[2023-09-02 14:56:36,381::train::INFO] [train] Iter 11085 | loss 0.9126 | loss(rot) 0.1777 | loss(pos) 0.4621 | loss(seq) 0.2728 | grad 5.9301 | lr 0.0010 | time_forward 2.7170 | time_backward 5.2270 |
[2023-09-02 14:56:47,260::train::INFO] [train] Iter 11086 | loss 0.9652 | loss(rot) 0.4256 | loss(pos) 0.4203 | loss(seq) 0.1192 | grad 3.4382 | lr 0.0010 | time_forward 3.3140 | time_backward 6.3570 |
[2023-09-02 14:56:56,680::train::INFO] [train] Iter 11087 | loss 1.6546 | loss(rot) 1.4942 | loss(pos) 0.1557 | loss(seq) 0.0047 | grad 4.3267 | lr 0.0010 | time_forward 3.8960 | time_backward 5.2860 |
[2023-09-02 14:57:08,454::train::INFO] [train] Iter 11088 | loss 1.6772 | loss(rot) 1.3946 | loss(pos) 0.1964 | loss(seq) 0.0862 | grad 4.0297 | lr 0.0010 | time_forward 6.3090 | time_backward 5.4610 |
[2023-09-02 14:57:18,729::train::INFO] [train] Iter 11089 | loss 0.7881 | loss(rot) 0.2232 | loss(pos) 0.4978 | loss(seq) 0.0672 | grad 4.2543 | lr 0.0010 | time_forward 4.3620 | time_backward 5.9090 |
[2023-09-02 14:57:26,915::train::INFO] [train] Iter 11090 | loss 2.5826 | loss(rot) 0.0071 | loss(pos) 2.5749 | loss(seq) 0.0005 | grad 7.9956 | lr 0.0010 | time_forward 3.4670 | time_backward 4.7160 |
[2023-09-02 14:57:29,545::train::INFO] [train] Iter 11091 | loss 1.3171 | loss(rot) 0.9459 | loss(pos) 0.3711 | loss(seq) 0.0000 | grad 5.5981 | lr 0.0010 | time_forward 1.2260 | time_backward 1.4010 |
[2023-09-02 14:57:42,405::train::INFO] [train] Iter 11092 | loss 1.2165 | loss(rot) 0.8491 | loss(pos) 0.0724 | loss(seq) 0.2950 | grad 3.9572 | lr 0.0010 | time_forward 7.0150 | time_backward 5.8410 |
[2023-09-02 14:57:50,570::train::INFO] [train] Iter 11093 | loss 2.3753 | loss(rot) 1.5369 | loss(pos) 0.4082 | loss(seq) 0.4301 | grad 4.8028 | lr 0.0010 | time_forward 3.4720 | time_backward 4.6890 |
[2023-09-02 14:57:57,815::train::INFO] [train] Iter 11094 | loss 2.7697 | loss(rot) 2.4223 | loss(pos) 0.3425 | loss(seq) 0.0048 | grad 4.3490 | lr 0.0010 | time_forward 3.0870 | time_backward 4.1540 |
[2023-09-02 14:58:00,597::train::INFO] [train] Iter 11095 | loss 2.0410 | loss(rot) 1.5075 | loss(pos) 0.1017 | loss(seq) 0.4317 | grad 4.8740 | lr 0.0010 | time_forward 1.3220 | time_backward 1.4570 |
[2023-09-02 14:58:03,274::train::INFO] [train] Iter 11096 | loss 1.1183 | loss(rot) 0.0731 | loss(pos) 1.0365 | loss(seq) 0.0087 | grad 6.3800 | lr 0.0010 | time_forward 1.2280 | time_backward 1.4450 |
[2023-09-02 14:58:10,982::train::INFO] [train] Iter 11097 | loss 1.7874 | loss(rot) 1.4763 | loss(pos) 0.1292 | loss(seq) 0.1819 | grad 6.5108 | lr 0.0010 | time_forward 3.1630 | time_backward 4.5420 |
[2023-09-02 14:58:13,303::train::INFO] [train] Iter 11098 | loss 2.0608 | loss(rot) 1.5550 | loss(pos) 0.1540 | loss(seq) 0.3518 | grad 3.4712 | lr 0.0010 | time_forward 1.0340 | time_backward 1.2850 |
[2023-09-02 14:58:16,028::train::INFO] [train] Iter 11099 | loss 1.9743 | loss(rot) 1.1684 | loss(pos) 0.2036 | loss(seq) 0.6023 | grad 3.6201 | lr 0.0010 | time_forward 1.2570 | time_backward 1.4640 |
[2023-09-02 14:58:25,072::train::INFO] [train] Iter 11100 | loss 0.8214 | loss(rot) 0.1254 | loss(pos) 0.6713 | loss(seq) 0.0247 | grad 3.6681 | lr 0.0010 | time_forward 3.6790 | time_backward 5.3610 |
[2023-09-02 14:58:27,738::train::INFO] [train] Iter 11101 | loss 0.5780 | loss(rot) 0.0837 | loss(pos) 0.4855 | loss(seq) 0.0089 | grad 4.1723 | lr 0.0010 | time_forward 1.2000 | time_backward 1.4630 |
[2023-09-02 14:58:35,680::train::INFO] [train] Iter 11102 | loss 1.6613 | loss(rot) 0.9771 | loss(pos) 0.1645 | loss(seq) 0.5198 | grad 4.3419 | lr 0.0010 | time_forward 3.3270 | time_backward 4.6120 |
[2023-09-02 14:58:38,516::train::INFO] [train] Iter 11103 | loss 1.5161 | loss(rot) 0.4522 | loss(pos) 0.3816 | loss(seq) 0.6823 | grad 3.6727 | lr 0.0010 | time_forward 1.3800 | time_backward 1.4520 |
[2023-09-02 14:58:41,231::train::INFO] [train] Iter 11104 | loss 2.3263 | loss(rot) 1.5472 | loss(pos) 0.2739 | loss(seq) 0.5052 | grad 5.2499 | lr 0.0010 | time_forward 1.2600 | time_backward 1.4510 |
[2023-09-02 14:58:43,838::train::INFO] [train] Iter 11105 | loss 1.3335 | loss(rot) 1.2823 | loss(pos) 0.0491 | loss(seq) 0.0021 | grad 7.2438 | lr 0.0010 | time_forward 1.2020 | time_backward 1.4020 |
[2023-09-02 14:58:51,529::train::INFO] [train] Iter 11106 | loss 1.7501 | loss(rot) 1.0063 | loss(pos) 0.3506 | loss(seq) 0.3931 | grad 4.8159 | lr 0.0010 | time_forward 3.2640 | time_backward 4.4210 |
[2023-09-02 14:58:59,814::train::INFO] [train] Iter 11107 | loss 0.7537 | loss(rot) 0.3359 | loss(pos) 0.2372 | loss(seq) 0.1807 | grad 5.9462 | lr 0.0010 | time_forward 3.5180 | time_backward 4.7450 |
[2023-09-02 14:59:02,432::train::INFO] [train] Iter 11108 | loss 1.9056 | loss(rot) 1.1116 | loss(pos) 0.2856 | loss(seq) 0.5085 | grad 4.4243 | lr 0.0010 | time_forward 1.2090 | time_backward 1.4070 |
[2023-09-02 14:59:11,799::train::INFO] [train] Iter 11109 | loss 1.3464 | loss(rot) 0.2856 | loss(pos) 0.4305 | loss(seq) 0.6303 | grad 4.0582 | lr 0.0010 | time_forward 3.8710 | time_backward 5.4920 |
[2023-09-02 14:59:22,656::train::INFO] [train] Iter 11110 | loss 1.0882 | loss(rot) 0.9246 | loss(pos) 0.0934 | loss(seq) 0.0702 | grad 4.6595 | lr 0.0010 | time_forward 5.0230 | time_backward 5.8320 |
[2023-09-02 14:59:25,291::train::INFO] [train] Iter 11111 | loss 1.7580 | loss(rot) 0.7015 | loss(pos) 0.3967 | loss(seq) 0.6598 | grad 3.5567 | lr 0.0010 | time_forward 1.2300 | time_backward 1.4010 |
[2023-09-02 14:59:32,552::train::INFO] [train] Iter 11112 | loss 1.6523 | loss(rot) 0.9202 | loss(pos) 0.1938 | loss(seq) 0.5384 | grad 4.0995 | lr 0.0010 | time_forward 2.9000 | time_backward 4.3570 |
[2023-09-02 14:59:41,044::train::INFO] [train] Iter 11113 | loss 2.5143 | loss(rot) 0.0629 | loss(pos) 2.4498 | loss(seq) 0.0016 | grad 6.8325 | lr 0.0010 | time_forward 3.5220 | time_backward 4.9660 |
[2023-09-02 14:59:43,683::train::INFO] [train] Iter 11114 | loss 2.6416 | loss(rot) 0.0457 | loss(pos) 2.5943 | loss(seq) 0.0016 | grad 6.7672 | lr 0.0010 | time_forward 1.2250 | time_backward 1.4110 |
[2023-09-02 14:59:51,650::train::INFO] [train] Iter 11115 | loss 1.8465 | loss(rot) 0.6650 | loss(pos) 0.3968 | loss(seq) 0.7847 | grad 4.7211 | lr 0.0010 | time_forward 3.3460 | time_backward 4.6190 |
[2023-09-02 14:59:54,401::train::INFO] [train] Iter 11116 | loss 3.8119 | loss(rot) 0.0409 | loss(pos) 3.7710 | loss(seq) 0.0000 | grad 9.3822 | lr 0.0010 | time_forward 1.2330 | time_backward 1.5150 |
[2023-09-02 15:00:03,799::train::INFO] [train] Iter 11117 | loss 2.1669 | loss(rot) 2.0920 | loss(pos) 0.0728 | loss(seq) 0.0021 | grad 5.6402 | lr 0.0010 | time_forward 3.9850 | time_backward 5.4090 |
[2023-09-02 15:00:12,169::train::INFO] [train] Iter 11118 | loss 2.0396 | loss(rot) 0.1047 | loss(pos) 1.9156 | loss(seq) 0.0194 | grad 8.4207 | lr 0.0010 | time_forward 3.4720 | time_backward 4.8940 |
[2023-09-02 15:00:20,077::train::INFO] [train] Iter 11119 | loss 1.5839 | loss(rot) 1.4561 | loss(pos) 0.1165 | loss(seq) 0.0113 | grad 10.0402 | lr 0.0010 | time_forward 3.1810 | time_backward 4.7190 |
[2023-09-02 15:00:28,758::train::INFO] [train] Iter 11120 | loss 1.1016 | loss(rot) 0.6442 | loss(pos) 0.1722 | loss(seq) 0.2851 | grad 5.1388 | lr 0.0010 | time_forward 3.7110 | time_backward 4.9670 |
[2023-09-02 15:00:38,460::train::INFO] [train] Iter 11121 | loss 1.5373 | loss(rot) 0.8437 | loss(pos) 0.3091 | loss(seq) 0.3844 | grad 4.0433 | lr 0.0010 | time_forward 3.6350 | time_backward 6.0640 |
[2023-09-02 15:00:41,064::train::INFO] [train] Iter 11122 | loss 2.8918 | loss(rot) 2.0971 | loss(pos) 0.2841 | loss(seq) 0.5106 | grad 4.0671 | lr 0.0010 | time_forward 1.2150 | time_backward 1.3870 |
[2023-09-02 15:00:52,098::train::INFO] [train] Iter 11123 | loss 1.2670 | loss(rot) 0.5189 | loss(pos) 0.2729 | loss(seq) 0.4752 | grad 5.0533 | lr 0.0010 | time_forward 4.4880 | time_backward 6.5320 |
[2023-09-02 15:00:54,781::train::INFO] [train] Iter 11124 | loss 1.6188 | loss(rot) 0.8976 | loss(pos) 0.3318 | loss(seq) 0.3894 | grad 8.9474 | lr 0.0010 | time_forward 1.2050 | time_backward 1.4750 |
[2023-09-02 15:00:57,398::train::INFO] [train] Iter 11125 | loss 1.8992 | loss(rot) 1.0618 | loss(pos) 0.2285 | loss(seq) 0.6089 | grad 4.5682 | lr 0.0010 | time_forward 1.2260 | time_backward 1.3740 |
[2023-09-02 15:01:00,079::train::INFO] [train] Iter 11126 | loss 1.6185 | loss(rot) 1.5138 | loss(pos) 0.0731 | loss(seq) 0.0316 | grad 11.9814 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4210 |
[2023-09-02 15:01:09,468::train::INFO] [train] Iter 11127 | loss 2.7477 | loss(rot) 2.3045 | loss(pos) 0.1975 | loss(seq) 0.2457 | grad 4.4248 | lr 0.0010 | time_forward 3.7600 | time_backward 5.6260 |
[2023-09-02 15:01:18,114::train::INFO] [train] Iter 11128 | loss 1.7966 | loss(rot) 0.9870 | loss(pos) 0.3313 | loss(seq) 0.4783 | grad 2.5304 | lr 0.0010 | time_forward 3.6500 | time_backward 4.9920 |
[2023-09-02 15:01:25,697::train::INFO] [train] Iter 11129 | loss 0.8249 | loss(rot) 0.2208 | loss(pos) 0.4317 | loss(seq) 0.1725 | grad 2.4123 | lr 0.0010 | time_forward 3.2740 | time_backward 4.3050 |
[2023-09-02 15:01:28,361::train::INFO] [train] Iter 11130 | loss 2.3066 | loss(rot) 1.3592 | loss(pos) 0.2468 | loss(seq) 0.7007 | grad 4.5210 | lr 0.0010 | time_forward 1.2350 | time_backward 1.4250 |
[2023-09-02 15:01:37,922::train::INFO] [train] Iter 11131 | loss 2.4689 | loss(rot) 2.3073 | loss(pos) 0.1613 | loss(seq) 0.0003 | grad 8.9213 | lr 0.0010 | time_forward 3.7960 | time_backward 5.7630 |
[2023-09-02 15:01:45,246::train::INFO] [train] Iter 11132 | loss 1.2740 | loss(rot) 0.3838 | loss(pos) 0.4699 | loss(seq) 0.4202 | grad 6.7255 | lr 0.0010 | time_forward 3.1220 | time_backward 4.1980 |
[2023-09-02 15:01:54,488::train::INFO] [train] Iter 11133 | loss 1.7691 | loss(rot) 1.1602 | loss(pos) 0.1658 | loss(seq) 0.4431 | grad 3.9643 | lr 0.0010 | time_forward 3.7070 | time_backward 5.5330 |
[2023-09-02 15:02:02,378::train::INFO] [train] Iter 11134 | loss 2.2087 | loss(rot) 2.0494 | loss(pos) 0.1462 | loss(seq) 0.0131 | grad 4.4836 | lr 0.0010 | time_forward 3.2400 | time_backward 4.6460 |
[2023-09-02 15:02:05,118::train::INFO] [train] Iter 11135 | loss 1.7930 | loss(rot) 1.2435 | loss(pos) 0.1289 | loss(seq) 0.4206 | grad 4.2445 | lr 0.0010 | time_forward 1.2610 | time_backward 1.4750 |
[2023-09-02 15:02:14,349::train::INFO] [train] Iter 11136 | loss 1.1950 | loss(rot) 0.0820 | loss(pos) 1.1028 | loss(seq) 0.0103 | grad 8.5905 | lr 0.0010 | time_forward 3.8550 | time_backward 5.3730 |
[2023-09-02 15:02:22,389::train::INFO] [train] Iter 11137 | loss 1.6468 | loss(rot) 1.5501 | loss(pos) 0.0861 | loss(seq) 0.0107 | grad 9.1769 | lr 0.0010 | time_forward 3.3880 | time_backward 4.6480 |
[2023-09-02 15:02:25,163::train::INFO] [train] Iter 11138 | loss 1.8395 | loss(rot) 1.0898 | loss(pos) 0.3201 | loss(seq) 0.4296 | grad 4.4979 | lr 0.0010 | time_forward 1.2530 | time_backward 1.5170 |
[2023-09-02 15:02:34,208::train::INFO] [train] Iter 11139 | loss 1.8908 | loss(rot) 1.6765 | loss(pos) 0.2136 | loss(seq) 0.0007 | grad 5.6533 | lr 0.0010 | time_forward 3.7600 | time_backward 5.2810 |
[2023-09-02 15:02:43,191::train::INFO] [train] Iter 11140 | loss 0.8323 | loss(rot) 0.1698 | loss(pos) 0.3649 | loss(seq) 0.2976 | grad 2.8493 | lr 0.0010 | time_forward 3.7960 | time_backward 5.1830 |
[2023-09-02 15:02:50,099::train::INFO] [train] Iter 11141 | loss 1.6616 | loss(rot) 1.4727 | loss(pos) 0.1654 | loss(seq) 0.0235 | grad 6.9761 | lr 0.0010 | time_forward 2.8710 | time_backward 4.0340 |
[2023-09-02 15:02:53,445::train::INFO] [train] Iter 11142 | loss 2.3510 | loss(rot) 2.1868 | loss(pos) 0.1462 | loss(seq) 0.0180 | grad 4.8860 | lr 0.0010 | time_forward 1.4210 | time_backward 1.9210 |
[2023-09-02 15:03:02,539::train::INFO] [train] Iter 11143 | loss 1.9195 | loss(rot) 1.2625 | loss(pos) 0.1647 | loss(seq) 0.4923 | grad 7.2806 | lr 0.0010 | time_forward 3.7800 | time_backward 5.3100 |
[2023-09-02 15:03:11,241::train::INFO] [train] Iter 11144 | loss 2.3939 | loss(rot) 1.6952 | loss(pos) 0.3420 | loss(seq) 0.3568 | grad 6.0829 | lr 0.0010 | time_forward 3.5680 | time_backward 5.1300 |
[2023-09-02 15:03:18,320::train::INFO] [train] Iter 11145 | loss 1.2708 | loss(rot) 0.5642 | loss(pos) 0.3348 | loss(seq) 0.3718 | grad 4.2352 | lr 0.0010 | time_forward 2.8990 | time_backward 4.1750 |
[2023-09-02 15:03:26,242::train::INFO] [train] Iter 11146 | loss 1.1884 | loss(rot) 0.0263 | loss(pos) 1.1586 | loss(seq) 0.0034 | grad 5.0802 | lr 0.0010 | time_forward 3.2730 | time_backward 4.6450 |
[2023-09-02 15:03:28,870::train::INFO] [train] Iter 11147 | loss 1.2335 | loss(rot) 0.9185 | loss(pos) 0.0505 | loss(seq) 0.2645 | grad 7.1837 | lr 0.0010 | time_forward 1.2190 | time_backward 1.4060 |
[2023-09-02 15:03:35,682::train::INFO] [train] Iter 11148 | loss 0.8483 | loss(rot) 0.2086 | loss(pos) 0.0901 | loss(seq) 0.5497 | grad 2.2746 | lr 0.0010 | time_forward 2.9030 | time_backward 3.8820 |
[2023-09-02 15:03:38,082::train::INFO] [train] Iter 11149 | loss 2.3257 | loss(rot) 1.8020 | loss(pos) 0.1002 | loss(seq) 0.4235 | grad 5.0987 | lr 0.0010 | time_forward 1.1200 | time_backward 1.2760 |
[2023-09-02 15:03:46,003::train::INFO] [train] Iter 11150 | loss 0.7577 | loss(rot) 0.1254 | loss(pos) 0.3623 | loss(seq) 0.2700 | grad 2.6474 | lr 0.0010 | time_forward 3.2360 | time_backward 4.6830 |
[2023-09-02 15:03:52,383::train::INFO] [train] Iter 11151 | loss 1.5333 | loss(rot) 1.2661 | loss(pos) 0.0548 | loss(seq) 0.2124 | grad 5.5959 | lr 0.0010 | time_forward 2.5050 | time_backward 3.8710 |
[2023-09-02 15:04:00,843::train::INFO] [train] Iter 11152 | loss 1.9123 | loss(rot) 1.7203 | loss(pos) 0.1859 | loss(seq) 0.0061 | grad 6.3316 | lr 0.0010 | time_forward 3.6340 | time_backward 4.8230 |
[2023-09-02 15:04:09,137::train::INFO] [train] Iter 11153 | loss 1.0967 | loss(rot) 0.3039 | loss(pos) 0.5325 | loss(seq) 0.2604 | grad 4.5538 | lr 0.0010 | time_forward 3.3510 | time_backward 4.9390 |
[2023-09-02 15:04:17,298::train::INFO] [train] Iter 11154 | loss 1.5045 | loss(rot) 0.8484 | loss(pos) 0.1909 | loss(seq) 0.4652 | grad 4.0458 | lr 0.0010 | time_forward 3.3940 | time_backward 4.7640 |
[2023-09-02 15:04:27,110::train::INFO] [train] Iter 11155 | loss 3.5829 | loss(rot) 2.6699 | loss(pos) 0.4956 | loss(seq) 0.4175 | grad 3.9920 | lr 0.0010 | time_forward 4.1850 | time_backward 5.6220 |
[2023-09-02 15:04:35,364::train::INFO] [train] Iter 11156 | loss 1.9817 | loss(rot) 1.3974 | loss(pos) 0.1143 | loss(seq) 0.4700 | grad 3.6489 | lr 0.0010 | time_forward 3.3800 | time_backward 4.8710 |
[2023-09-02 15:04:38,109::train::INFO] [train] Iter 11157 | loss 1.3535 | loss(rot) 0.6287 | loss(pos) 0.3655 | loss(seq) 0.3593 | grad 3.9795 | lr 0.0010 | time_forward 1.3280 | time_backward 1.4110 |
[2023-09-02 15:04:40,762::train::INFO] [train] Iter 11158 | loss 1.0661 | loss(rot) 0.4211 | loss(pos) 0.3737 | loss(seq) 0.2712 | grad 3.1979 | lr 0.0010 | time_forward 1.2070 | time_backward 1.4430 |
[2023-09-02 15:04:49,306::train::INFO] [train] Iter 11159 | loss 1.1322 | loss(rot) 0.0862 | loss(pos) 1.0302 | loss(seq) 0.0158 | grad 5.7582 | lr 0.0010 | time_forward 3.6560 | time_backward 4.8850 |
[2023-09-02 15:04:52,551::train::INFO] [train] Iter 11160 | loss 2.0366 | loss(rot) 1.7831 | loss(pos) 0.1497 | loss(seq) 0.1038 | grad 3.8394 | lr 0.0010 | time_forward 1.4210 | time_backward 1.8190 |
[2023-09-02 15:05:03,676::train::INFO] [train] Iter 11161 | loss 1.0106 | loss(rot) 0.8784 | loss(pos) 0.1186 | loss(seq) 0.0136 | grad 5.1600 | lr 0.0010 | time_forward 5.4770 | time_backward 5.6440 |
[2023-09-02 15:05:11,166::train::INFO] [train] Iter 11162 | loss 1.0227 | loss(rot) 0.4993 | loss(pos) 0.2412 | loss(seq) 0.2822 | grad 3.8766 | lr 0.0010 | time_forward 3.2070 | time_backward 4.2790 |
[2023-09-02 15:05:20,679::train::INFO] [train] Iter 11163 | loss 1.1118 | loss(rot) 0.2165 | loss(pos) 0.8743 | loss(seq) 0.0211 | grad 3.1705 | lr 0.0010 | time_forward 3.9530 | time_backward 5.5570 |
[2023-09-02 15:05:28,279::train::INFO] [train] Iter 11164 | loss 2.1065 | loss(rot) 1.6997 | loss(pos) 0.1411 | loss(seq) 0.2657 | grad 4.9111 | lr 0.0010 | time_forward 3.2090 | time_backward 4.3870 |
[2023-09-02 15:05:36,974::train::INFO] [train] Iter 11165 | loss 0.7040 | loss(rot) 0.1678 | loss(pos) 0.2400 | loss(seq) 0.2962 | grad 2.3013 | lr 0.0010 | time_forward 3.8580 | time_backward 4.8330 |
[2023-09-02 15:05:44,709::train::INFO] [train] Iter 11166 | loss 0.7738 | loss(rot) 0.2111 | loss(pos) 0.3084 | loss(seq) 0.2543 | grad 3.2294 | lr 0.0010 | time_forward 3.1200 | time_backward 4.6110 |
[2023-09-02 15:05:52,945::train::INFO] [train] Iter 11167 | loss 1.1800 | loss(rot) 1.0314 | loss(pos) 0.0560 | loss(seq) 0.0925 | grad 5.1608 | lr 0.0010 | time_forward 3.3760 | time_backward 4.8570 |
[2023-09-02 15:05:55,776::train::INFO] [train] Iter 11168 | loss 0.7060 | loss(rot) 0.3240 | loss(pos) 0.2814 | loss(seq) 0.1006 | grad 3.6074 | lr 0.0010 | time_forward 1.3610 | time_backward 1.4650 |
[2023-09-02 15:06:03,972::train::INFO] [train] Iter 11169 | loss 0.9492 | loss(rot) 0.5732 | loss(pos) 0.0714 | loss(seq) 0.3046 | grad 3.5365 | lr 0.0010 | time_forward 3.4680 | time_backward 4.7250 |
[2023-09-02 15:06:12,636::train::INFO] [train] Iter 11170 | loss 1.1492 | loss(rot) 0.6664 | loss(pos) 0.1167 | loss(seq) 0.3661 | grad 3.6612 | lr 0.0010 | time_forward 3.5680 | time_backward 5.0920 |
[2023-09-02 15:06:19,165::train::INFO] [train] Iter 11171 | loss 2.6044 | loss(rot) 2.4725 | loss(pos) 0.0903 | loss(seq) 0.0416 | grad 9.2140 | lr 0.0010 | time_forward 2.5540 | time_backward 3.9710 |
[2023-09-02 15:06:27,434::train::INFO] [train] Iter 11172 | loss 0.9968 | loss(rot) 0.4056 | loss(pos) 0.1322 | loss(seq) 0.4591 | grad 3.9481 | lr 0.0010 | time_forward 3.4130 | time_backward 4.8520 |
[2023-09-02 15:06:35,177::train::INFO] [train] Iter 11173 | loss 2.1866 | loss(rot) 1.7064 | loss(pos) 0.1113 | loss(seq) 0.3689 | grad 7.7517 | lr 0.0010 | time_forward 3.1780 | time_backward 4.5620 |
[2023-09-02 15:06:43,759::train::INFO] [train] Iter 11174 | loss 1.3609 | loss(rot) 0.9808 | loss(pos) 0.0460 | loss(seq) 0.3341 | grad 3.5484 | lr 0.0010 | time_forward 3.7310 | time_backward 4.8460 |
[2023-09-02 15:06:51,625::train::INFO] [train] Iter 11175 | loss 2.3400 | loss(rot) 2.1065 | loss(pos) 0.2118 | loss(seq) 0.0217 | grad 3.1809 | lr 0.0010 | time_forward 3.3250 | time_backward 4.5360 |
[2023-09-02 15:06:54,249::train::INFO] [train] Iter 11176 | loss 1.1034 | loss(rot) 0.3202 | loss(pos) 0.4603 | loss(seq) 0.3229 | grad 4.6828 | lr 0.0010 | time_forward 1.2370 | time_backward 1.3830 |
[2023-09-02 15:07:02,506::train::INFO] [train] Iter 11177 | loss 0.8493 | loss(rot) 0.1420 | loss(pos) 0.3964 | loss(seq) 0.3109 | grad 4.2682 | lr 0.0010 | time_forward 3.5170 | time_backward 4.7360 |
[2023-09-02 15:07:05,193::train::INFO] [train] Iter 11178 | loss 1.1057 | loss(rot) 1.0056 | loss(pos) 0.0790 | loss(seq) 0.0211 | grad 5.2813 | lr 0.0010 | time_forward 1.2710 | time_backward 1.4120 |
[2023-09-02 15:07:11,612::train::INFO] [train] Iter 11179 | loss 1.2519 | loss(rot) 0.8183 | loss(pos) 0.1397 | loss(seq) 0.2939 | grad 6.8312 | lr 0.0010 | time_forward 2.7000 | time_backward 3.6940 |
[2023-09-02 15:07:14,122::train::INFO] [train] Iter 11180 | loss 1.4890 | loss(rot) 0.9085 | loss(pos) 0.0959 | loss(seq) 0.4846 | grad 5.2025 | lr 0.0010 | time_forward 1.1720 | time_backward 1.3340 |
[2023-09-02 15:07:17,384::train::INFO] [train] Iter 11181 | loss 0.8650 | loss(rot) 0.4452 | loss(pos) 0.2178 | loss(seq) 0.2019 | grad 2.4423 | lr 0.0010 | time_forward 1.3930 | time_backward 1.8560 |
[2023-09-02 15:07:26,795::train::INFO] [train] Iter 11182 | loss 2.2359 | loss(rot) 1.9877 | loss(pos) 0.1986 | loss(seq) 0.0497 | grad 9.4627 | lr 0.0010 | time_forward 3.7780 | time_backward 5.6300 |
[2023-09-02 15:07:36,183::train::INFO] [train] Iter 11183 | loss 1.8485 | loss(rot) 0.9146 | loss(pos) 0.2905 | loss(seq) 0.6434 | grad 4.2895 | lr 0.0010 | time_forward 3.9140 | time_backward 5.4690 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.