text
stringlengths 56
1.16k
|
|---|
[2023-09-03 02:49:49,026::train::INFO] [train] Iter 17078 | loss 1.2914 | loss(rot) 0.4977 | loss(pos) 0.4093 | loss(seq) 0.3844 | grad 4.3347 | lr 0.0010 | time_forward 3.0720 | time_backward 4.6750 |
[2023-09-03 02:49:57,954::train::INFO] [train] Iter 17079 | loss 1.6211 | loss(rot) 0.1287 | loss(pos) 1.4897 | loss(seq) 0.0027 | grad 6.2425 | lr 0.0010 | time_forward 3.3180 | time_backward 5.6070 |
[2023-09-03 02:50:00,607::train::INFO] [train] Iter 17080 | loss 1.9973 | loss(rot) 1.3409 | loss(pos) 0.3706 | loss(seq) 0.2858 | grad 4.6718 | lr 0.0010 | time_forward 1.2200 | time_backward 1.4290 |
[2023-09-03 02:50:03,171::train::INFO] [train] Iter 17081 | loss 1.5003 | loss(rot) 0.0786 | loss(pos) 1.4169 | loss(seq) 0.0047 | grad 5.5825 | lr 0.0010 | time_forward 1.1740 | time_backward 1.3860 |
[2023-09-03 02:50:11,677::train::INFO] [train] Iter 17082 | loss 1.1702 | loss(rot) 0.4349 | loss(pos) 0.2420 | loss(seq) 0.4933 | grad 3.3885 | lr 0.0010 | time_forward 3.5480 | time_backward 4.9550 |
[2023-09-03 02:50:19,591::train::INFO] [train] Iter 17083 | loss 1.1411 | loss(rot) 0.7908 | loss(pos) 0.2116 | loss(seq) 0.1386 | grad 6.2299 | lr 0.0010 | time_forward 3.4030 | time_backward 4.5070 |
[2023-09-03 02:50:28,175::train::INFO] [train] Iter 17084 | loss 0.9241 | loss(rot) 0.8339 | loss(pos) 0.0616 | loss(seq) 0.0285 | grad 6.5924 | lr 0.0010 | time_forward 3.5810 | time_backward 5.0000 |
[2023-09-03 02:50:36,698::train::INFO] [train] Iter 17085 | loss 1.5240 | loss(rot) 0.3849 | loss(pos) 1.1236 | loss(seq) 0.0154 | grad 6.3981 | lr 0.0010 | time_forward 3.4330 | time_backward 5.0760 |
[2023-09-03 02:50:45,145::train::INFO] [train] Iter 17086 | loss 0.5594 | loss(rot) 0.1552 | loss(pos) 0.3607 | loss(seq) 0.0435 | grad 3.1217 | lr 0.0010 | time_forward 3.5840 | time_backward 4.8610 |
[2023-09-03 02:50:52,448::train::INFO] [train] Iter 17087 | loss 2.7472 | loss(rot) 2.0639 | loss(pos) 0.2173 | loss(seq) 0.4660 | grad 6.2249 | lr 0.0010 | time_forward 3.1400 | time_backward 4.1590 |
[2023-09-03 02:51:00,700::train::INFO] [train] Iter 17088 | loss 1.2815 | loss(rot) 0.3119 | loss(pos) 0.6495 | loss(seq) 0.3202 | grad 6.0293 | lr 0.0010 | time_forward 3.5120 | time_backward 4.7360 |
[2023-09-03 02:51:08,267::train::INFO] [train] Iter 17089 | loss 1.2210 | loss(rot) 0.1344 | loss(pos) 0.2950 | loss(seq) 0.7915 | grad 3.9813 | lr 0.0010 | time_forward 3.1670 | time_backward 4.3970 |
[2023-09-03 02:51:11,009::train::INFO] [train] Iter 17090 | loss 1.5015 | loss(rot) 0.1010 | loss(pos) 1.3931 | loss(seq) 0.0074 | grad 12.8981 | lr 0.0010 | time_forward 1.2780 | time_backward 1.4600 |
[2023-09-03 02:51:19,632::train::INFO] [train] Iter 17091 | loss 1.1813 | loss(rot) 0.4173 | loss(pos) 0.7329 | loss(seq) 0.0310 | grad 5.1048 | lr 0.0010 | time_forward 3.6030 | time_backward 5.0180 |
[2023-09-03 02:51:28,505::train::INFO] [train] Iter 17092 | loss 1.3912 | loss(rot) 0.0248 | loss(pos) 1.3627 | loss(seq) 0.0036 | grad 6.3431 | lr 0.0010 | time_forward 3.6200 | time_backward 5.2490 |
[2023-09-03 02:51:31,151::train::INFO] [train] Iter 17093 | loss 1.4493 | loss(rot) 1.2336 | loss(pos) 0.1801 | loss(seq) 0.0355 | grad 18.6679 | lr 0.0010 | time_forward 1.2440 | time_backward 1.3990 |
[2023-09-03 02:51:40,000::train::INFO] [train] Iter 17094 | loss 1.6031 | loss(rot) 0.3056 | loss(pos) 0.8790 | loss(seq) 0.4185 | grad 8.0574 | lr 0.0010 | time_forward 3.3630 | time_backward 5.4820 |
[2023-09-03 02:51:47,669::train::INFO] [train] Iter 17095 | loss 0.9402 | loss(rot) 0.1871 | loss(pos) 0.4536 | loss(seq) 0.2995 | grad 5.3554 | lr 0.0010 | time_forward 3.2670 | time_backward 4.3930 |
[2023-09-03 02:51:53,065::train::INFO] [train] Iter 17096 | loss 1.6285 | loss(rot) 0.2538 | loss(pos) 0.8868 | loss(seq) 0.4879 | grad 8.3084 | lr 0.0010 | time_forward 2.1630 | time_backward 3.2280 |
[2023-09-03 02:51:55,751::train::INFO] [train] Iter 17097 | loss 0.6125 | loss(rot) 0.3223 | loss(pos) 0.2901 | loss(seq) 0.0000 | grad 5.6497 | lr 0.0010 | time_forward 1.2380 | time_backward 1.4450 |
[2023-09-03 02:52:04,398::train::INFO] [train] Iter 17098 | loss 0.8236 | loss(rot) 0.2925 | loss(pos) 0.3944 | loss(seq) 0.1367 | grad 5.0438 | lr 0.0010 | time_forward 3.3960 | time_backward 5.2470 |
[2023-09-03 02:52:07,006::train::INFO] [train] Iter 17099 | loss 1.9800 | loss(rot) 1.5596 | loss(pos) 0.2759 | loss(seq) 0.1446 | grad 4.4549 | lr 0.0010 | time_forward 1.2210 | time_backward 1.3840 |
[2023-09-03 02:52:10,095::train::INFO] [train] Iter 17100 | loss 1.5701 | loss(rot) 0.7508 | loss(pos) 0.3085 | loss(seq) 0.5108 | grad 3.5183 | lr 0.0010 | time_forward 1.3250 | time_backward 1.7600 |
[2023-09-03 02:52:17,949::train::INFO] [train] Iter 17101 | loss 1.0047 | loss(rot) 0.2602 | loss(pos) 0.2837 | loss(seq) 0.4608 | grad 4.3418 | lr 0.0010 | time_forward 3.3040 | time_backward 4.5470 |
[2023-09-03 02:52:25,741::train::INFO] [train] Iter 17102 | loss 1.5108 | loss(rot) 1.4219 | loss(pos) 0.0766 | loss(seq) 0.0123 | grad 14.5735 | lr 0.0010 | time_forward 3.4150 | time_backward 4.3730 |
[2023-09-03 02:52:28,376::train::INFO] [train] Iter 17103 | loss 1.9591 | loss(rot) 1.1828 | loss(pos) 0.2181 | loss(seq) 0.5582 | grad 5.5487 | lr 0.0010 | time_forward 1.2160 | time_backward 1.4160 |
[2023-09-03 02:52:36,126::train::INFO] [train] Iter 17104 | loss 0.9712 | loss(rot) 0.4667 | loss(pos) 0.2406 | loss(seq) 0.2640 | grad 4.2603 | lr 0.0010 | time_forward 3.2740 | time_backward 4.4720 |
[2023-09-03 02:52:43,881::train::INFO] [train] Iter 17105 | loss 0.8149 | loss(rot) 0.1156 | loss(pos) 0.6871 | loss(seq) 0.0122 | grad 3.4467 | lr 0.0010 | time_forward 3.3490 | time_backward 4.4040 |
[2023-09-03 02:52:47,213::train::INFO] [train] Iter 17106 | loss 1.0866 | loss(rot) 0.4540 | loss(pos) 0.3481 | loss(seq) 0.2845 | grad 2.6095 | lr 0.0010 | time_forward 1.4300 | time_backward 1.8990 |
[2023-09-03 02:52:55,380::train::INFO] [train] Iter 17107 | loss 1.5290 | loss(rot) 0.6059 | loss(pos) 0.4488 | loss(seq) 0.4743 | grad 3.9674 | lr 0.0010 | time_forward 3.5760 | time_backward 4.5830 |
[2023-09-03 02:53:02,158::train::INFO] [train] Iter 17108 | loss 1.6770 | loss(rot) 1.4158 | loss(pos) 0.1523 | loss(seq) 0.1090 | grad 4.2898 | lr 0.0010 | time_forward 2.9280 | time_backward 3.8470 |
[2023-09-03 02:53:11,154::train::INFO] [train] Iter 17109 | loss 0.9970 | loss(rot) 0.1812 | loss(pos) 0.6029 | loss(seq) 0.2129 | grad 5.2375 | lr 0.0010 | time_forward 3.7010 | time_backward 5.2920 |
[2023-09-03 02:53:20,265::train::INFO] [train] Iter 17110 | loss 2.1600 | loss(rot) 0.3873 | loss(pos) 1.0708 | loss(seq) 0.7019 | grad 6.4909 | lr 0.0010 | time_forward 3.7630 | time_backward 5.3320 |
[2023-09-03 02:53:23,547::train::INFO] [train] Iter 17111 | loss 2.8370 | loss(rot) 1.8411 | loss(pos) 0.3986 | loss(seq) 0.5972 | grad 3.9431 | lr 0.0010 | time_forward 1.3920 | time_backward 1.8870 |
[2023-09-03 02:53:30,697::train::INFO] [train] Iter 17112 | loss 0.4840 | loss(rot) 0.3558 | loss(pos) 0.1270 | loss(seq) 0.0012 | grad 6.4292 | lr 0.0010 | time_forward 3.0750 | time_backward 4.0720 |
[2023-09-03 02:53:39,782::train::INFO] [train] Iter 17113 | loss 2.0149 | loss(rot) 1.1623 | loss(pos) 0.4230 | loss(seq) 0.4296 | grad 4.3401 | lr 0.0010 | time_forward 3.8560 | time_backward 5.2260 |
[2023-09-03 02:53:47,782::train::INFO] [train] Iter 17114 | loss 1.8426 | loss(rot) 0.6827 | loss(pos) 0.6523 | loss(seq) 0.5076 | grad 6.5905 | lr 0.0010 | time_forward 3.4080 | time_backward 4.5890 |
[2023-09-03 02:53:50,360::train::INFO] [train] Iter 17115 | loss 0.8927 | loss(rot) 0.2049 | loss(pos) 0.2022 | loss(seq) 0.4856 | grad 3.8584 | lr 0.0010 | time_forward 1.1830 | time_backward 1.3790 |
[2023-09-03 02:53:57,776::train::INFO] [train] Iter 17116 | loss 1.6470 | loss(rot) 1.2651 | loss(pos) 0.1067 | loss(seq) 0.2752 | grad 3.6734 | lr 0.0010 | time_forward 3.0390 | time_backward 4.3730 |
[2023-09-03 02:54:06,724::train::INFO] [train] Iter 17117 | loss 2.7951 | loss(rot) 1.8118 | loss(pos) 0.4331 | loss(seq) 0.5502 | grad 3.9642 | lr 0.0010 | time_forward 3.6970 | time_backward 5.2490 |
[2023-09-03 02:54:09,822::train::INFO] [train] Iter 17118 | loss 1.4932 | loss(rot) 0.0195 | loss(pos) 1.4703 | loss(seq) 0.0034 | grad 8.1056 | lr 0.0010 | time_forward 1.3420 | time_backward 1.7520 |
[2023-09-03 02:54:16,670::train::INFO] [train] Iter 17119 | loss 1.3520 | loss(rot) 0.5055 | loss(pos) 0.3422 | loss(seq) 0.5043 | grad 6.5246 | lr 0.0010 | time_forward 2.8620 | time_backward 3.9830 |
[2023-09-03 02:54:24,172::train::INFO] [train] Iter 17120 | loss 1.7993 | loss(rot) 0.7336 | loss(pos) 0.5783 | loss(seq) 0.4874 | grad 6.0867 | lr 0.0010 | time_forward 3.1880 | time_backward 4.3100 |
[2023-09-03 02:54:31,775::train::INFO] [train] Iter 17121 | loss 1.2577 | loss(rot) 0.0683 | loss(pos) 1.1787 | loss(seq) 0.0107 | grad 6.3429 | lr 0.0010 | time_forward 3.0830 | time_backward 4.5170 |
[2023-09-03 02:54:39,593::train::INFO] [train] Iter 17122 | loss 1.0399 | loss(rot) 0.2163 | loss(pos) 0.3506 | loss(seq) 0.4730 | grad 3.7084 | lr 0.0010 | time_forward 3.2070 | time_backward 4.6090 |
[2023-09-03 02:54:42,628::train::INFO] [train] Iter 17123 | loss 2.1214 | loss(rot) 1.3722 | loss(pos) 0.3279 | loss(seq) 0.4212 | grad 4.7659 | lr 0.0010 | time_forward 1.3430 | time_backward 1.6880 |
[2023-09-03 02:54:45,257::train::INFO] [train] Iter 17124 | loss 0.6073 | loss(rot) 0.0481 | loss(pos) 0.5501 | loss(seq) 0.0091 | grad 3.9963 | lr 0.0010 | time_forward 1.2200 | time_backward 1.4060 |
[2023-09-03 02:54:48,721::train::INFO] [train] Iter 17125 | loss 2.6435 | loss(rot) 1.7205 | loss(pos) 0.3934 | loss(seq) 0.5296 | grad 3.8367 | lr 0.0010 | time_forward 1.4960 | time_backward 1.9660 |
[2023-09-03 02:54:50,900::train::INFO] [train] Iter 17126 | loss 1.5412 | loss(rot) 0.0825 | loss(pos) 1.1399 | loss(seq) 0.3189 | grad 6.6853 | lr 0.0010 | time_forward 1.0130 | time_backward 1.1620 |
[2023-09-03 02:54:53,120::train::INFO] [train] Iter 17127 | loss 1.1478 | loss(rot) 0.0206 | loss(pos) 1.1266 | loss(seq) 0.0006 | grad 5.1806 | lr 0.0010 | time_forward 1.0390 | time_backward 1.1780 |
[2023-09-03 02:55:00,897::train::INFO] [train] Iter 17128 | loss 1.6256 | loss(rot) 0.8755 | loss(pos) 0.1753 | loss(seq) 0.5748 | grad 7.6762 | lr 0.0010 | time_forward 3.1460 | time_backward 4.6280 |
[2023-09-03 02:55:09,712::train::INFO] [train] Iter 17129 | loss 2.9460 | loss(rot) 2.7736 | loss(pos) 0.1703 | loss(seq) 0.0021 | grad 4.3480 | lr 0.0010 | time_forward 3.4640 | time_backward 5.3490 |
[2023-09-03 02:55:12,293::train::INFO] [train] Iter 17130 | loss 2.0545 | loss(rot) 1.7726 | loss(pos) 0.1496 | loss(seq) 0.1323 | grad 5.1042 | lr 0.0010 | time_forward 1.1860 | time_backward 1.3910 |
[2023-09-03 02:55:21,228::train::INFO] [train] Iter 17131 | loss 1.4086 | loss(rot) 0.5833 | loss(pos) 0.2464 | loss(seq) 0.5789 | grad 4.9188 | lr 0.0010 | time_forward 3.6170 | time_backward 5.3150 |
[2023-09-03 02:55:28,995::train::INFO] [train] Iter 17132 | loss 1.3742 | loss(rot) 0.9504 | loss(pos) 0.1328 | loss(seq) 0.2910 | grad 4.4791 | lr 0.0010 | time_forward 3.1540 | time_backward 4.6100 |
[2023-09-03 02:55:37,026::train::INFO] [train] Iter 17133 | loss 1.5458 | loss(rot) 0.7567 | loss(pos) 0.2412 | loss(seq) 0.5479 | grad 6.2429 | lr 0.0010 | time_forward 3.2310 | time_backward 4.7960 |
[2023-09-03 02:55:44,429::train::INFO] [train] Iter 17134 | loss 1.4643 | loss(rot) 0.2627 | loss(pos) 1.1343 | loss(seq) 0.0674 | grad 4.0856 | lr 0.0010 | time_forward 3.1520 | time_backward 4.2470 |
[2023-09-03 02:55:49,674::train::INFO] [train] Iter 17135 | loss 2.2829 | loss(rot) 1.3992 | loss(pos) 0.3483 | loss(seq) 0.5354 | grad 4.0718 | lr 0.0010 | time_forward 2.2160 | time_backward 3.0250 |
[2023-09-03 02:55:58,365::train::INFO] [train] Iter 17136 | loss 2.5022 | loss(rot) 1.5198 | loss(pos) 0.2731 | loss(seq) 0.7094 | grad 5.8960 | lr 0.0010 | time_forward 3.4690 | time_backward 5.2200 |
[2023-09-03 02:56:07,141::train::INFO] [train] Iter 17137 | loss 1.4753 | loss(rot) 0.6059 | loss(pos) 0.4510 | loss(seq) 0.4185 | grad 3.5274 | lr 0.0010 | time_forward 3.7800 | time_backward 4.9920 |
[2023-09-03 02:56:09,372::train::INFO] [train] Iter 17138 | loss 1.2021 | loss(rot) 0.2816 | loss(pos) 0.3079 | loss(seq) 0.6125 | grad 2.6919 | lr 0.0010 | time_forward 1.0660 | time_backward 1.1630 |
[2023-09-03 02:56:17,849::train::INFO] [train] Iter 17139 | loss 1.5289 | loss(rot) 0.4395 | loss(pos) 0.7453 | loss(seq) 0.3441 | grad 3.9267 | lr 0.0010 | time_forward 3.3800 | time_backward 5.0940 |
[2023-09-03 02:56:26,417::train::INFO] [train] Iter 17140 | loss 0.7727 | loss(rot) 0.1219 | loss(pos) 0.6034 | loss(seq) 0.0473 | grad 4.9652 | lr 0.0010 | time_forward 3.3970 | time_backward 5.1680 |
[2023-09-03 02:56:34,122::train::INFO] [train] Iter 17141 | loss 0.9844 | loss(rot) 0.2599 | loss(pos) 0.2737 | loss(seq) 0.4507 | grad 3.5310 | lr 0.0010 | time_forward 3.1300 | time_backward 4.5710 |
[2023-09-03 02:56:41,554::train::INFO] [train] Iter 17142 | loss 1.0979 | loss(rot) 0.9579 | loss(pos) 0.1036 | loss(seq) 0.0363 | grad 5.1903 | lr 0.0010 | time_forward 3.1490 | time_backward 4.2800 |
[2023-09-03 02:56:44,180::train::INFO] [train] Iter 17143 | loss 1.8480 | loss(rot) 1.2633 | loss(pos) 0.5847 | loss(seq) 0.0000 | grad 9.3442 | lr 0.0010 | time_forward 1.2290 | time_backward 1.3930 |
[2023-09-03 02:56:47,244::train::INFO] [train] Iter 17144 | loss 2.2491 | loss(rot) 1.4115 | loss(pos) 0.3225 | loss(seq) 0.5152 | grad 4.1502 | lr 0.0010 | time_forward 1.3390 | time_backward 1.7220 |
[2023-09-03 02:56:49,853::train::INFO] [train] Iter 17145 | loss 0.9678 | loss(rot) 0.6322 | loss(pos) 0.1011 | loss(seq) 0.2345 | grad 3.2566 | lr 0.0010 | time_forward 1.1720 | time_backward 1.4340 |
[2023-09-03 02:56:56,769::train::INFO] [train] Iter 17146 | loss 1.0574 | loss(rot) 0.2196 | loss(pos) 0.7061 | loss(seq) 0.1317 | grad 3.8420 | lr 0.0010 | time_forward 2.8930 | time_backward 4.0200 |
[2023-09-03 02:56:59,427::train::INFO] [train] Iter 17147 | loss 0.7855 | loss(rot) 0.6512 | loss(pos) 0.1199 | loss(seq) 0.0144 | grad 10.7455 | lr 0.0010 | time_forward 1.2310 | time_backward 1.4240 |
[2023-09-03 02:57:06,941::train::INFO] [train] Iter 17148 | loss 1.2087 | loss(rot) 0.7617 | loss(pos) 0.1306 | loss(seq) 0.3165 | grad 7.8292 | lr 0.0010 | time_forward 3.1850 | time_backward 4.3250 |
[2023-09-03 02:57:14,092::train::INFO] [train] Iter 17149 | loss 1.8577 | loss(rot) 0.8007 | loss(pos) 0.5867 | loss(seq) 0.4702 | grad 4.0182 | lr 0.0010 | time_forward 3.0600 | time_backward 4.0870 |
[2023-09-03 02:57:23,053::train::INFO] [train] Iter 17150 | loss 1.0293 | loss(rot) 0.1579 | loss(pos) 0.8565 | loss(seq) 0.0150 | grad 4.8903 | lr 0.0010 | time_forward 3.6320 | time_backward 5.3210 |
[2023-09-03 02:57:30,092::train::INFO] [train] Iter 17151 | loss 0.6661 | loss(rot) 0.1975 | loss(pos) 0.1911 | loss(seq) 0.2775 | grad 3.6401 | lr 0.0010 | time_forward 2.9530 | time_backward 4.0760 |
[2023-09-03 02:57:39,164::train::INFO] [train] Iter 17152 | loss 1.7297 | loss(rot) 0.9280 | loss(pos) 0.2392 | loss(seq) 0.5624 | grad 4.6297 | lr 0.0010 | time_forward 3.5610 | time_backward 5.5080 |
[2023-09-03 02:57:47,464::train::INFO] [train] Iter 17153 | loss 2.0896 | loss(rot) 0.0246 | loss(pos) 2.0643 | loss(seq) 0.0007 | grad 6.4906 | lr 0.0010 | time_forward 3.5280 | time_backward 4.7680 |
[2023-09-03 02:57:56,610::train::INFO] [train] Iter 17154 | loss 1.5942 | loss(rot) 0.9017 | loss(pos) 0.1546 | loss(seq) 0.5379 | grad 4.0906 | lr 0.0010 | time_forward 3.7300 | time_backward 5.4130 |
[2023-09-03 02:58:04,506::train::INFO] [train] Iter 17155 | loss 1.0775 | loss(rot) 0.2865 | loss(pos) 0.4355 | loss(seq) 0.3554 | grad 4.9682 | lr 0.0010 | time_forward 3.3080 | time_backward 4.5840 |
[2023-09-03 02:58:13,200::train::INFO] [train] Iter 17156 | loss 2.0621 | loss(rot) 1.4730 | loss(pos) 0.3095 | loss(seq) 0.2797 | grad 6.6616 | lr 0.0010 | time_forward 3.5800 | time_backward 5.1110 |
[2023-09-03 02:58:21,109::train::INFO] [train] Iter 17157 | loss 1.2478 | loss(rot) 0.4615 | loss(pos) 0.2516 | loss(seq) 0.5347 | grad 4.8683 | lr 0.0010 | time_forward 3.4050 | time_backward 4.5000 |
[2023-09-03 02:58:28,822::train::INFO] [train] Iter 17158 | loss 0.7227 | loss(rot) 0.1137 | loss(pos) 0.5703 | loss(seq) 0.0386 | grad 5.5025 | lr 0.0010 | time_forward 3.0390 | time_backward 4.6700 |
[2023-09-03 02:58:37,955::train::INFO] [train] Iter 17159 | loss 1.5016 | loss(rot) 0.7903 | loss(pos) 0.2998 | loss(seq) 0.4114 | grad 6.9009 | lr 0.0010 | time_forward 3.4660 | time_backward 5.6650 |
[2023-09-03 02:58:46,324::train::INFO] [train] Iter 17160 | loss 0.8300 | loss(rot) 0.2200 | loss(pos) 0.2336 | loss(seq) 0.3763 | grad 3.4527 | lr 0.0010 | time_forward 3.2380 | time_backward 5.1280 |
[2023-09-03 02:58:53,120::train::INFO] [train] Iter 17161 | loss 2.8927 | loss(rot) 2.6950 | loss(pos) 0.0678 | loss(seq) 0.1299 | grad 5.8206 | lr 0.0010 | time_forward 2.8950 | time_backward 3.8970 |
[2023-09-03 02:58:55,533::train::INFO] [train] Iter 17162 | loss 2.0477 | loss(rot) 1.8873 | loss(pos) 0.1179 | loss(seq) 0.0425 | grad 7.1246 | lr 0.0010 | time_forward 1.1380 | time_backward 1.2720 |
[2023-09-03 02:59:04,734::train::INFO] [train] Iter 17163 | loss 1.1117 | loss(rot) 0.2937 | loss(pos) 0.7983 | loss(seq) 0.0197 | grad 5.3525 | lr 0.0010 | time_forward 3.8080 | time_backward 5.3900 |
[2023-09-03 02:59:14,553::train::INFO] [train] Iter 17164 | loss 2.5342 | loss(rot) 2.3083 | loss(pos) 0.1980 | loss(seq) 0.0279 | grad 3.8059 | lr 0.0010 | time_forward 3.8690 | time_backward 5.9460 |
[2023-09-03 02:59:24,104::train::INFO] [train] Iter 17165 | loss 1.2026 | loss(rot) 0.3946 | loss(pos) 0.4925 | loss(seq) 0.3155 | grad 4.8293 | lr 0.0010 | time_forward 3.9300 | time_backward 5.6180 |
[2023-09-03 02:59:31,769::train::INFO] [train] Iter 17166 | loss 1.5059 | loss(rot) 0.9749 | loss(pos) 0.1406 | loss(seq) 0.3903 | grad 5.3443 | lr 0.0010 | time_forward 3.2180 | time_backward 4.4430 |
[2023-09-03 02:59:34,463::train::INFO] [train] Iter 17167 | loss 1.8328 | loss(rot) 1.1656 | loss(pos) 0.1910 | loss(seq) 0.4761 | grad 8.2617 | lr 0.0010 | time_forward 1.2450 | time_backward 1.4450 |
[2023-09-03 02:59:40,393::train::INFO] [train] Iter 17168 | loss 1.7324 | loss(rot) 0.7152 | loss(pos) 0.3768 | loss(seq) 0.6404 | grad 6.8942 | lr 0.0010 | time_forward 2.5400 | time_backward 3.3860 |
[2023-09-03 02:59:49,642::train::INFO] [train] Iter 17169 | loss 1.7893 | loss(rot) 1.2998 | loss(pos) 0.1124 | loss(seq) 0.3772 | grad 4.2816 | lr 0.0010 | time_forward 3.7430 | time_backward 5.5020 |
[2023-09-03 02:59:52,301::train::INFO] [train] Iter 17170 | loss 0.9927 | loss(rot) 0.8854 | loss(pos) 0.0874 | loss(seq) 0.0200 | grad 5.4152 | lr 0.0010 | time_forward 1.2340 | time_backward 1.4220 |
[2023-09-03 03:00:01,366::train::INFO] [train] Iter 17171 | loss 0.9974 | loss(rot) 0.2122 | loss(pos) 0.4494 | loss(seq) 0.3359 | grad 4.4765 | lr 0.0010 | time_forward 3.6210 | time_backward 5.4410 |
[2023-09-03 03:00:07,671::train::INFO] [train] Iter 17172 | loss 1.5910 | loss(rot) 0.0172 | loss(pos) 1.5696 | loss(seq) 0.0042 | grad 6.0525 | lr 0.0010 | time_forward 2.6460 | time_backward 3.6560 |
[2023-09-03 03:00:16,764::train::INFO] [train] Iter 17173 | loss 1.5268 | loss(rot) 0.1300 | loss(pos) 1.3910 | loss(seq) 0.0058 | grad 5.7491 | lr 0.0010 | time_forward 3.6630 | time_backward 5.4270 |
[2023-09-03 03:00:24,721::train::INFO] [train] Iter 17174 | loss 1.1438 | loss(rot) 0.7894 | loss(pos) 0.0875 | loss(seq) 0.2668 | grad 2.6809 | lr 0.0010 | time_forward 3.4140 | time_backward 4.5400 |
[2023-09-03 03:00:33,755::train::INFO] [train] Iter 17175 | loss 1.9788 | loss(rot) 1.1692 | loss(pos) 0.3444 | loss(seq) 0.4651 | grad 6.1267 | lr 0.0010 | time_forward 3.7760 | time_backward 5.2530 |
[2023-09-03 03:00:43,081::train::INFO] [train] Iter 17176 | loss 1.2990 | loss(rot) 0.3697 | loss(pos) 0.5099 | loss(seq) 0.4194 | grad 3.9166 | lr 0.0010 | time_forward 3.9410 | time_backward 5.3810 |
[2023-09-03 03:00:52,281::train::INFO] [train] Iter 17177 | loss 1.7251 | loss(rot) 0.7001 | loss(pos) 0.3883 | loss(seq) 0.6367 | grad 3.3515 | lr 0.0010 | time_forward 3.5790 | time_backward 5.6160 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.