text:
RotNetR验证集的loss降不下来

使用训练参数
lr = 0.01
momentum = 0.9
epochs = 300
steps = 128
训练集：验证集70%:30% 总共约1100张去白边钱币
素材预览

测试角度都是错的，该如何优化
<2023-08-18 13:57:37.784> [INFO] Epoch#0. time_cost: 32.89 s. train_loss: 5.39984232. val_loss: 5.36462307 <2023-08-18 13:57:55.110> [INFO] Epoch#1. time_cost: 48.94 s. train_loss: 5.35185540. val_loss: 5.38960536 <2023-08-18 13:58:11.490> [INFO] Epoch#2. time_cost: 64.82 s. train_loss: 5.28306431. val_loss: 5.41948541 <2023-08-18 13:58:28.066> [INFO] Epoch#3. time_cost: 80.73 s. train_loss: 5.21700990. val_loss: 5.41810258 <2023-08-18 13:58:44.535> [INFO] Epoch#4. time_cost: 96.61 s. train_loss: 5.15511119. val_loss: 5.41366911 <2023-08-18 13:59:01.068> [INFO] Epoch#5. time_cost: 112.57 s. train_loss: 5.06084383. val_loss: 5.40824318 <2023-08-18 13:59:17.673> [INFO] Epoch#6. time_cost: 128.55 s. train_loss: 4.97198361. val_loss: 5.40104500 <2023-08-18 13:59:34.405> [INFO] Epoch#7. time_cost: 144.58 s. train_loss: 4.89924210. val_loss: 5.39258560 <2023-08-18 13:59:51.164> [INFO] Epoch#8. time_cost: 160.73 s. train_loss: 4.81734937. val_loss: 5.38335117 <2023-08-18 14:00:08.268> [INFO] Epoch#9. time_cost: 177.25 s. train_loss: 4.71686673. val_loss: 5.37633689 <2023-08-18 14:00:24.879> [INFO] Epoch#10. time_cost: 193.28 s. train_loss: 4.65475571. val_loss: 5.37279606 <2023-08-18 14:00:41.440> [INFO] Epoch#11. time_cost: 209.35 s. train_loss: 4.55467379. val_loss: 5.36483145 <2023-08-18 14:00:58.116> [INFO] Epoch#12. time_cost: 225.54 s. train_loss: 4.47352850. val_loss: 5.35914739 <2023-08-18 14:01:15.269> [INFO] Epoch#13. time_cost: 241.79 s. train_loss: 4.41723913. val_loss: 5.35702181 <2023-08-18 14:01:32.629> [INFO] Epoch#14. time_cost: 258.01 s. train_loss: 4.31735170. val_loss: 5.35660950 <2023-08-18 14:01:49.672> [INFO] Epoch#15. time_cost: 274.19 s. train_loss: 4.23688066. val_loss: 5.35307089 <2023-08-18 14:02:06.594> [INFO] Epoch#16. time_cost: 290.31 s. train_loss: 4.18479526. val_loss: 5.35310698 <2023-08-18 14:02:23.423> [INFO] Epoch#17. time_cost: 306.63 s. train_loss: 4.08692378. val_loss: 5.34825500 <2023-08-18 14:02:40.599> [INFO] Epoch#18. time_cost: 322.81 s. train_loss: 4.01058915. val_loss: 5.34844208 <2023-08-18 14:02:57.059> [INFO] Epoch#19. time_cost: 338.78 s. train_loss: 3.92014989. val_loss: 5.34933837 <2023-08-18 14:03:13.625> [INFO] Epoch#20. time_cost: 354.79 s. train_loss: 3.84989792. val_loss: 5.34641616 <2023-08-18 14:03:30.806> [INFO] Epoch#21. time_cost: 371.08 s. train_loss: 3.75730199. val_loss: 5.34646304 <2023-08-18 14:03:47.440> [INFO] Epoch#22. time_cost: 387.23 s. train_loss: 3.69406390. val_loss: 5.34865459 <2023-08-18 14:04:04.430> [INFO] Epoch#23. time_cost: 403.58 s. train_loss: 3.58468822. val_loss: 5.34718132 <2023-08-18 14:04:21.192> [INFO] Epoch#24. time_cost: 419.76 s. train_loss: 3.53524327. val_loss: 5.35206652 <2023-08-18 14:04:37.991> [INFO] Epoch#25. time_cost: 435.94 s. train_loss: 3.43011984. val_loss: 5.34888013 <2023-08-18 14:04:54.962> [INFO] Epoch#26. time_cost: 452.26 s. train_loss: 3.37779093. val_loss: 5.34560808 <2023-08-18 14:05:11.900> [INFO] Epoch#27. time_cost: 468.42 s. train_loss: 3.25674978. val_loss: 5.34464852 <2023-08-18 14:05:28.939> [INFO] Epoch#28. time_cost: 484.61 s. train_loss: 3.21313724. val_loss: 5.34361029 <2023-08-18 14:05:46.150> [INFO] Epoch#29. time_cost: 500.94 s. train_loss: 3.11788267. val_loss: 5.34200462 <2023-08-18 14:06:03.293> [INFO] Epoch#30. time_cost: 517.17 s. train_loss: 3.03583649. val_loss: 5.34520737 <2023-08-18 14:06:20.056> [INFO] Epoch#31. time_cost: 533.36 s. train_loss: 3.02078411. val_loss: 5.34665251 <2023-08-18 14:06:36.799> [INFO] Epoch#32. time_cost: 549.56 s. train_loss: 2.89035311. val_loss: 5.34966660 <2023-08-18 14:06:53.613> [INFO] Epoch#33. time_cost: 565.89 s. train_loss: 2.80607820. val_loss: 5.34706863 <2023-08-18 14:07:10.240> [INFO] Epoch#34. time_cost: 582.04 s. train_loss: 2.75119737. val_loss: 5.34983921 <2023-08-18 14:07:27.220> [INFO] Epoch#35. time_cost: 598.46 s. train_loss: 2.66276371. val_loss: 5.34948270 <2023-08-18 14:07:43.911> [INFO] Epoch#36. time_cost: 614.61 s. train_loss: 2.62098613. val_loss: 5.34578753 <2023-08-18 14:08:00.852> [INFO] Epoch#37. time_cost: 631.00 s. train_loss: 2.56218195. val_loss: 5.35035912 <2023-08-18 14:08:17.749> [INFO] Epoch#38. time_cost: 647.41 s. train_loss: 2.44102857. val_loss: 5.35140228 <2023-08-18 14:08:34.332> [INFO] Epoch#39. time_cost: 663.49 s. train_loss: 2.41077507. val_loss: 5.35149717 <2023-08-18 14:08:50.979> [INFO] Epoch#40. time_cost: 679.53 s. train_loss: 2.29778108. val_loss: 5.35420863 <2023-08-18 14:09:07.607> [INFO] Epoch#41. time_cost: 695.53 s. train_loss: 2.24829721. val_loss: 5.35726897 <2023-08-18 14:09:24.769> [INFO] Epoch#42. time_cost: 712.05 s. train_loss: 2.17894366. val_loss: 5.35133410 <2023-08-18 14:09:41.792> [INFO] Epoch#43. time_cost: 728.57 s. train_loss: 2.12200819. val_loss: 5.35315466 <2023-08-18 14:09:58.563> [INFO] Epoch#44. time_cost: 744.72 s. train_loss: 2.07205370. val_loss: 5.34872564 <2023-08-18 14:10:15.165> [INFO] Epoch#45. time_cost: 760.83 s. train_loss: 1.99733144. val_loss: 5.34793647 <2023-08-18 14:10:32.038> [INFO] Epoch#46. time_cost: 777.10 s. train_loss: 1.95925856. val_loss: 5.34805107 <2023-08-18 14:10:49.068> [INFO] Epoch#47. time_cost: 793.60 s. train_loss: 1.88353942. val_loss: 5.34884135 <2023-08-18 14:11:05.784> [INFO] Epoch#48. time_cost: 809.81 s. train_loss: 1.83938010. val_loss: 5.35141436 <2023-08-18 14:11:22.648> [INFO] Epoch#49. time_cost: 826.09 s. train_loss: 1.79292178. val_loss: 5.34627692 <2023-08-18 14:11:39.304> [INFO] Epoch#50. time_cost: 842.29 s. train_loss: 1.68158425. val_loss: 5.34675503 <2023-08-18 14:11:55.872> [INFO] Epoch#51. time_cost: 858.40 s. train_loss: 1.62872086. val_loss: 5.34923681 <2023-08-18 14:12:12.628> [INFO] Epoch#52. time_cost: 874.63 s. train_loss: 1.59070106. val_loss: 5.35235278 <2023-08-18 14:12:29.318> [INFO] Epoch#53. time_cost: 890.81 s. train_loss: 1.58161676. val_loss: 5.35133855 <2023-08-18 14:12:46.143> [INFO] Epoch#54. time_cost: 907.11 s. train_loss: 1.45260657. val_loss: 5.34634527 <2023-08-18 14:13:02.999> [INFO] Epoch#55. time_cost: 923.48 s. train_loss: 1.47507057. val_loss: 5.34150346 <2023-08-18 14:13:20.303> [INFO] Epoch#56. time_cost: 939.80 s. train_loss: 1.38150918. val_loss: 5.34167035 <2023-08-18 14:13:36.953> [INFO] Epoch#57. time_cost: 955.96 s. train_loss: 1.36797532. val_loss: 5.33937915 <2023-08-18 14:13:54.243> [INFO] Epoch#58. time_cost: 972.27 s. train_loss: 1.28458185. val_loss: 5.34360949 <2023-08-18 14:14:11.088> [INFO] Epoch#59. time_cost: 988.53 s. train_loss: 1.24424957. val_loss: 5.34156942 <2023-08-18 14:14:27.971> [INFO] Epoch#60. time_cost: 1004.85 s. train_loss: 1.18852963. val_loss: 5.33656184 <2023-08-18 14:14:44.796> [INFO] Epoch#61. time_cost: 1020.81 s. train_loss: 1.13654406. val_loss: 5.33123016 <2023-08-18 14:15:02.092> [INFO] Epoch#62. time_cost: 1037.30 s. train_loss: 1.11281125. val_loss: 5.33135033 <2023-08-18 14:15:19.106> [INFO] Epoch#63. time_cost: 1053.65 s. train_loss: 1.05972394. val_loss: 5.33335686 <2023-08-18 14:15:35.944> [INFO] Epoch#64. time_cost: 1069.97 s. train_loss: 1.02143451. val_loss: 5.33008957 <2023-08-18 14:15:52.917> [INFO] Epoch#65. time_cost: 1086.13 s. train_loss: 1.01201259. val_loss: 5.32418426 <2023-08-18 14:16:10.429> [INFO] Epoch#66. time_cost: 1102.75 s. train_loss: 0.97423361. val_loss: 5.32560396 <2023-08-18 14:16:26.975> [INFO] Epoch#67. time_cost: 1118.77 s. train_loss: 0.98155708. val_loss: 5.32422972 <2023-08-18 14:16:43.738> [INFO] Epoch#68. time_cost: 1134.93 s. train_loss: 0.89851645. val_loss: 5.32218838 <2023-08-18 14:17:00.692> [INFO] Epoch#69. time_cost: 1151.01 s. train_loss: 0.90840406. val_loss: 5.32185570 <2023-08-18 14:17:17.757> [INFO] Epoch#70. time_cost: 1167.22 s. train_loss: 0.84972844. val_loss: 5.31999524 <2023-08-18 14:17:34.972> [INFO] Epoch#71. time_cost: 1183.57 s. train_loss: 0.82386800. val_loss: 5.31885560 <2023-08-18 14:17:52.144> [INFO] Epoch#72. time_cost: 1199.80 s. train_loss: 0.79325682. val_loss: 5.32391421 <2023-08-18 14:18:09.007> [INFO] Epoch#73. time_cost: 1216.09 s. train_loss: 0.75515443. val_loss: 5.32378785 <2023-08-18 14:18:25.720> [INFO] Epoch#74. time_cost: 1232.24 s. train_loss: 0.71768803. val_loss: 5.31845586 <2023-08-18 14:18:42.934> [INFO] Epoch#75. time_cost: 1248.52 s. train_loss: 0.74559616. val_loss: 5.31854550 <2023-08-18 14:18:59.665> [INFO] Epoch#76. time_cost: 1264.72 s. train_loss: 0.68202233. val_loss: 5.31668854 <2023-08-18 14:19:16.607> [INFO] Epoch#77. time_cost: 1280.92 s. train_loss: 0.65233253. val_loss: 5.31342141 <2023-08-18 14:19:33.557> [INFO] Epoch#78. time_cost: 1296.93 s. train_loss: 0.64974236. val_loss: 5.30892277 <2023-08-18 14:19:50.504> [INFO] Epoch#79. time_cost: 1313.06 s. train_loss: 0.58420439. val_loss: 5.30771160 <2023-08-18 14:20:07.773> [INFO] Epoch#80. time_cost: 1329.33 s. train_loss: 0.60893153. val_loss: 5.30896378 <2023-08-18 14:20:24.518> [INFO] Epoch#81. time_cost: 1345.57 s. train_loss: 0.57614258. val_loss: 5.30884250 <2023-08-18 14:20:41.130> [INFO] Epoch#82. time_cost: 1361.64 s. train_loss: 0.53065565. val_loss: 5.30851078 <2023-08-18 14:20:58.022> [INFO] Epoch#83. time_cost: 1377.96 s. train_loss: 0.55741334. val_loss: 5.30744998 <2023-08-18 14:21:14.904> [INFO] Epoch#84. time_cost: 1393.96 s. train_loss: 0.48479454. val_loss: 5.30663506 <2023-08-18 14:21:32.100> [INFO] Epoch#85. time_cost: 1410.37 s. train_loss: 0.47788703. val_loss: 5.30053854 <2023-08-18 14:21:49.213> [INFO] Epoch#86. time_cost: 1426.53 s. train_loss: 0.48423344. val_loss: 5.29945850 <2023-08-18 14:22:06.621> [INFO] Epoch#87. time_cost: 1442.83 s. train_loss: 0.44990043. val_loss: 5.30025927 <2023-08-18 14:22:23.385> [INFO] Epoch#88. time_cost: 1458.98 s. train_loss: 0.44953348. val_loss: 5.29905891 <2023-08-18 14:22:40.358> [INFO] Epoch#89. time_cost: 1474.94 s. train_loss: 0.43896342. val_loss: 5.30130974 <2023-08-18 14:22:57.296> [INFO] Epoch#90. time_cost: 1491.33 s. train_loss: 0.40358200. val_loss: 5.30313905 <2023-08-18 14:23:14.203> [INFO] Epoch#91. time_cost: 1507.74 s. train_loss: 0.38030928. val_loss: 5.30020889 <2023-08-18 14:23:31.249> [INFO] Epoch#92. time_cost: 1524.19 s. train_loss: 0.36855397. val_loss: 5.29769659 <2023-08-18 14:23:48.304> [INFO] Epoch#93. time_cost: 1540.40 s. train_loss: 0.39343657. val_loss: 5.29662418 <2023-08-18 14:24:05.624> [INFO] Epoch#94. time_cost: 1556.82 s. train_loss: 0.35523611. val_loss: 5.29806995 <2023-08-18 14:24:22.604> [INFO] Epoch#95. time_cost: 1573.18 s. train_loss: 0.38470637. val_loss: 5.29897292 <2023-08-18 14:24:39.336> [INFO] Epoch#96. time_cost: 1589.40 s. train_loss: 0.35899499. val_loss: 5.30027898 <2023-08-18 14:24:56.202> [INFO] Epoch#97. time_cost: 1605.79 s. train_loss: 0.33074107. val_loss: 5.29959599 <2023-08-18 14:25:12.997> [INFO] Epoch#98. time_cost: 1622.00 s. train_loss: 0.31499263. val_loss: 5.29822334 <2023-08-18 14:25:29.901> [INFO] Epoch#99. time_cost: 1638.42 s. train_loss: 0.31733391. val_loss: 5.30082989 <2023-08-18 14:25:46.728> [INFO] Epoch#100. time_cost: 1654.55 s. train_loss: 0.32080573. val_loss: 5.29806964 <2023-08-18 14:26:03.189> [INFO] Epoch#101. time_cost: 1670.50 s. train_loss: 0.30004243. val_loss: 5.29328775 <2023-08-18 14:26:20.261> [INFO] Epoch#102. time_cost: 1686.65 s. train_loss: 0.29755034. val_loss: 5.29279629 <2023-08-18 14:26:37.467> [INFO] Epoch#103. time_cost: 1702.96 s. train_loss: 0.28089099. val_loss: 5.29503473 <2023-08-18 14:26:54.288> [INFO] Epoch#104. time_cost: 1719.07 s. train_loss: 0.28207827. val_loss: 5.29597712 <2023-08-18 14:27:11.176> [INFO] Epoch#105. time_cost: 1735.35 s. train_loss: 0.26215186. val_loss: 5.29589160 <2023-08-18 14:27:27.886> [INFO] Epoch#106. time_cost: 1751.49 s. train_loss: 0.25227861. val_loss: 5.29495859 <2023-08-18 14:27:44.839> [INFO] Epoch#107. time_cost: 1767.86 s. train_loss: 0.27683722. val_loss: 5.29369672 <2023-08-18 14:28:01.458> [INFO] Epoch#108. time_cost: 1783.96 s. train_loss: 0.24133206. val_loss: 5.29565271 <2023-08-18 14:28:18.337> [INFO] Epoch#109. time_cost: 1800.35 s. train_loss: 0.23433661. val_loss: 5.29376634 <2023-08-18 14:28:34.999> [INFO] Epoch#110. time_cost: 1816.54 s. train_loss: 0.23755440. val_loss: 5.29343255 <2023-08-18 14:28:51.802> [INFO] Epoch#111. time_cost: 1832.89 s. train_loss: 0.22447769. val_loss: 5.29052639 <2023-08-18 14:29:08.952> [INFO] Epoch#112. time_cost: 1849.30 s. train_loss: 0.23238146. val_loss: 5.29428466 <2023-08-18 14:29:25.784> [INFO] Epoch#113. time_cost: 1865.56 s. train_loss: 0.22170261. val_loss: 5.29492776 <2023-08-18 14:29:42.793> [INFO] Epoch#114. time_cost: 1882.06 s. train_loss: 0.21545648. val_loss: 5.29312213 <2023-08-18 14:29:59.412> [INFO] Epoch#115. time_cost: 1898.21 s. train_loss: 0.20180116. val_loss: 5.29303535 <2023-08-18 14:30:16.259> [INFO] Epoch#116. time_cost: 1914.53 s. train_loss: 0.19667596. val_loss: 5.29073985 <2023-08-18 14:30:33.082> [INFO] Epoch#117. time_cost: 1930.81 s. train_loss: 0.20202053. val_loss: 5.29082489 <2023-08-18 14:30:49.921> [INFO] Epoch#118. time_cost: 1947.08 s. train_loss: 0.19160863. val_loss: 5.29071124 <2023-08-18 14:31:06.598> [INFO] Epoch#119. time_cost: 1963.19 s. train_loss: 0.19253234. val_loss: 5.29217211 <2023-08-18 14:31:23.262> [INFO] Epoch#120. time_cost: 1979.35 s. train_loss: 0.20008727. val_loss: 5.29381418 <2023-08-18 14:31:40.064> [INFO] Epoch#121. time_cost: 1995.64 s. train_loss: 0.18423576. val_loss: 5.29436827 <2023-08-18 14:31:57.018> [INFO] Epoch#122. time_cost: 2012.03 s. train_loss: 0.18706201. val_loss: 5.29476261 <2023-08-18 14:32:13.724> [INFO] Epoch#123. time_cost: 2028.22 s. train_loss: 0.17524794. val_loss: 5.29642137 <2023-08-18 14:32:30.843> [INFO] Epoch#124. time_cost: 2044.73 s. train_loss: 0.17649993. val_loss: 5.29553525 <2023-08-18 14:32:47.986> [INFO] Epoch#125. time_cost: 2061.28 s. train_loss: 0.16367954. val_loss: 5.29705667 <2023-08-18 14:33:04.760> [INFO] Epoch#126. time_cost: 2077.57 s. train_loss: 0.16235787. val_loss: 5.29822620 <2023-08-18 14:33:21.836> [INFO] Epoch#127. time_cost: 2094.10 s. train_loss: 0.15805034. val_loss: 5.29666471 <2023-08-18 14:33:38.651> [INFO] Epoch#128. time_cost: 2110.38 s. train_loss: 0.16695243. val_loss: 5.29671876 <2023-08-18 14:33:55.408> [INFO] Epoch#129. time_cost: 2126.55 s. train_loss: 0.14886506. val_loss: 5.29992644 <2023-08-18 14:34:12.275> [INFO] Epoch#130. time_cost: 2142.95 s. train_loss: 0.14749421. val_loss: 5.29999638 <2023-08-18 14:34:29.053> [INFO] Epoch#131. time_cost: 2159.16 s. train_loss: 0.16307789. val_loss: 5.29719162 <2023-08-18 14:34:45.788> [INFO] Epoch#132. time_cost: 2175.27 s. train_loss: 0.14282146. val_loss: 5.29932435 <2023-08-18 14:35:02.511> [INFO] Epoch#133. time_cost: 2191.50 s. train_loss: 0.14902505. val_loss: 5.29678647 <2023-08-18 14:35:19.176> [INFO] Epoch#134. time_cost: 2207.62 s. train_loss: 0.16553759. val_loss: 5.29835685 <2023-08-18 14:35:36.006> [INFO] Epoch#135. time_cost: 2223.88 s. train_loss: 0.13034127. val_loss: 5.29728015 <2023-08-18 14:35:52.973> [INFO] Epoch#136. time_cost: 2240.26 s. train_loss: 0.13669445. val_loss: 5.29960585 <2023-08-18 14:36:09.725> [INFO] Epoch#137. time_cost: 2256.47 s. train_loss: 0.13718895. val_loss: 5.30132929 <2023-08-18 14:36:26.503> [INFO] Epoch#138. time_cost: 2272.64 s. train_loss: 0.13112331. val_loss: 5.30023400

这种一般是学到了什么奇怪的特征，要不先全部用正面或者全部用反面来试试？记得把DataLoader的shuffle设成True，不随机的话更容易学歪

单面相同图像训练还是一样无法收敛，已经打开了shuffle设成True

<2023-08-18 16:52:27.948> [INFO] Epoch#0. time_cost: 11.26 s. train_loss: 5.30330102. val_loss: 5.32662797 <2023-08-18 16:52:33.756> [INFO] Epoch#1. time_cost: 16.06 s. train_loss: 5.26328055. val_loss: 5.38382506 <2023-08-18 16:52:39.128> [INFO] Epoch#2. time_cost: 20.85 s. train_loss: 5.20550839. val_loss: 5.31866765 <2023-08-18 16:52:44.975> [INFO] Epoch#3. time_cost: 25.73 s. train_loss: 5.07200551. val_loss: 5.38940692 <2023-08-18 16:52:50.417> [INFO] Epoch#4. time_cost: 30.53 s. train_loss: 4.94194833. val_loss: 5.35936069 <2023-08-18 16:52:55.881> [INFO] Epoch#5. time_cost: 35.36 s. train_loss: 4.83208760. val_loss: 5.32527280 <2023-08-18 16:53:01.330> [INFO] Epoch#6. time_cost: 40.19 s. train_loss: 4.71431120. val_loss: 5.40086532 <2023-08-18 16:53:06.812> [INFO] Epoch#7. time_cost: 45.03 s. train_loss: 4.48791242. val_loss: 5.35610247 <2023-08-18 16:53:12.485> [INFO] Epoch#8. time_cost: 50.08 s. train_loss: 4.32728696. val_loss: 5.38680983 <2023-08-18 16:53:17.952> [INFO] Epoch#9. time_cost: 54.94 s. train_loss: 4.13140206. val_loss: 5.36251259 <2023-08-18 16:53:23.518> [INFO] Epoch#10. time_cost: 59.77 s. train_loss: 3.97570495. val_loss: 5.32798958 <2023-08-18 16:53:29.088> [INFO] Epoch#11. time_cost: 64.74 s. train_loss: 3.82368414. val_loss: 5.36804605 <2023-08-18 16:53:34.854> [INFO] Epoch#12. time_cost: 69.90 s. train_loss: 3.66186694. val_loss: 5.29402494 <2023-08-18 16:53:40.715> [INFO] Epoch#13. time_cost: 74.82 s. train_loss: 3.46259562. val_loss: 5.31973505 <2023-08-18 16:53:46.188> [INFO] Epoch#14. time_cost: 79.68 s. train_loss: 3.34844212. val_loss: 5.29534698 <2023-08-18 16:53:51.751> [INFO] Epoch#15. time_cost: 84.59 s. train_loss: 3.12492613. val_loss: 5.39548421 <2023-08-18 16:53:57.348> [INFO] Epoch#16. time_cost: 89.56 s. train_loss: 2.98319157. val_loss: 5.40921092 <2023-08-18 16:54:02.957> [INFO] Epoch#17. time_cost: 94.47 s. train_loss: 2.84793460. val_loss: 5.34042692 <2023-08-18 16:54:08.591> [INFO] Epoch#18. time_cost: 99.37 s. train_loss: 2.65782102. val_loss: 5.36988568 <2023-08-18 16:54:14.213> [INFO] Epoch#19. time_cost: 104.32 s. train_loss: 2.52567454. val_loss: 5.33618855 <2023-08-18 16:54:19.883> [INFO] Epoch#20. time_cost: 109.29 s. train_loss: 2.35375420. val_loss: 5.31661797 <2023-08-18 16:54:25.426> [INFO] Epoch#21. time_cost: 114.18 s. train_loss: 2.25517937. val_loss: 5.37782240 <2023-08-18 16:54:30.948> [INFO] Epoch#22. time_cost: 119.09 s. train_loss: 2.05319774. val_loss: 5.36982632 <2023-08-18 16:54:36.424> [INFO] Epoch#23. time_cost: 123.95 s. train_loss: 1.93894289. val_loss: 5.39411950 <2023-08-18 16:54:42.007> [INFO] Epoch#24. time_cost: 128.88 s. train_loss: 1.76177744. val_loss: 5.34503984 <2023-08-18 16:54:47.459> [INFO] Epoch#25. time_cost: 133.70 s. train_loss: 1.74932716. val_loss: 5.22905540 <2023-08-18 16:54:53.228> [INFO] Epoch#26. time_cost: 138.54 s. train_loss: 1.56955890. val_loss: 5.32931566 <2023-08-18 16:54:58.849> [INFO] Epoch#27. time_cost: 143.47 s. train_loss: 1.49377288. val_loss: 5.37630987 <2023-08-18 16:55:04.515> [INFO] Epoch#28. time_cost: 148.42 s. train_loss: 1.33536245. val_loss: 5.35716152 <2023-08-18 16:55:10.284> [INFO] Epoch#29. time_cost: 153.42 s. train_loss: 1.32433244. val_loss: 5.38517284 <2023-08-18 16:55:15.962> [INFO] Epoch#30. time_cost: 158.48 s. train_loss: 1.14002415. val_loss: 5.52969408 <2023-08-18 16:55:21.536> [INFO] Epoch#31. time_cost: 163.43 s. train_loss: 1.11591223. val_loss: 5.44804335 <2023-08-18 16:55:27.003> [INFO] Epoch#32. time_cost: 168.30 s. train_loss: 1.01697875. val_loss: 5.22046757 <2023-08-18 16:55:32.841> [INFO] Epoch#33. time_cost: 173.26 s. train_loss: 0.95621278. val_loss: 5.43956280 <2023-08-18 16:55:38.391> [INFO] Epoch#34. time_cost: 178.15 s. train_loss: 0.81955061. val_loss: 5.47637320 <2023-08-18 16:55:43.865> [INFO] Epoch#35. time_cost: 183.01 s. train_loss: 0.75798803. val_loss: 5.41070747 <2023-08-18 16:55:49.375> [INFO] Epoch#36. time_cost: 187.90 s. train_loss: 0.72421407. val_loss: 5.15936089 <2023-08-18 16:55:55.175> [INFO] Epoch#37. time_cost: 192.77 s. train_loss: 0.68637379. val_loss: 5.37881804 <2023-08-18 16:56:00.713> [INFO] Epoch#38. time_cost: 197.66 s. train_loss: 0.65562144. val_loss: 5.26347351 <2023-08-18 16:56:06.249> [INFO] Epoch#39. time_cost: 202.59 s. train_loss: 0.56010985. val_loss: 5.29488277 <2023-08-18 16:56:11.710> [INFO] Epoch#40. time_cost: 207.44 s. train_loss: 0.59256203. val_loss: 5.26576877 <2023-08-18 16:56:17.202> [INFO] Epoch#41. time_cost: 212.32 s. train_loss: 0.52209971. val_loss: 5.45372605 <2023-08-18 16:56:22.794> [INFO] Epoch#42. time_cost: 217.24 s. train_loss: 0.54710286. val_loss: 5.31848598 <2023-08-18 16:56:28.444> [INFO] Epoch#43. time_cost: 222.27 s. train_loss: 0.44930240. val_loss: 5.51086330 <2023-08-18 16:56:33.973> [INFO] Epoch#44. time_cost: 227.18 s. train_loss: 0.39940043. val_loss: 5.34111023 <2023-08-18 16:56:39.596> [INFO] Epoch#45. time_cost: 232.18 s. train_loss: 0.39297298. val_loss: 5.43975902 <2023-08-18 16:56:45.118> [INFO] Epoch#46. time_cost: 237.06 s. train_loss: 0.34562535. val_loss: 5.47232461 <2023-08-18 16:56:50.623> [INFO] Epoch#47. time_cost: 241.95 s. train_loss: 0.34902160. val_loss: 5.39059401 <2023-08-18 16:56:56.181> [INFO] Epoch#48. time_cost: 246.91 s. train_loss: 0.31559715. val_loss: 5.29491401 <2023-08-18 16:57:01.753> [INFO] Epoch#49. time_cost: 251.86 s. train_loss: 0.31622377. val_loss: 5.41225433 <2023-08-18 16:57:07.428> [INFO] Epoch#50. time_cost: 256.90 s. train_loss: 0.27491240. val_loss: 5.36622477 <2023-08-18 16:57:13.041> [INFO] Epoch#51. time_cost: 261.90 s. train_loss: 0.26355119. val_loss: 5.40250540 <2023-08-18 16:57:18.648> [INFO] Epoch#52. time_cost: 266.88 s. train_loss: 0.27926902. val_loss: 5.48708844 <2023-08-18 16:57:24.032> [INFO] Epoch#53. time_cost: 271.68 s. train_loss: 0.23514211. val_loss: 5.40322447 <2023-08-18 16:57:29.688> [INFO] Epoch#54. time_cost: 276.72 s. train_loss: 0.22060247. val_loss: 5.42269874 <2023-08-18 16:57:35.271> [INFO] Epoch#55. time_cost: 281.60 s. train_loss: 0.22819298. val_loss: 5.53691554 <2023-08-18 16:57:40.751> [INFO] Epoch#56. time_cost: 286.48 s. train_loss: 0.23862205. val_loss: 5.37165403 <2023-08-18 16:57:46.264> [INFO] Epoch#57. time_cost: 291.37 s. train_loss: 0.19607902. val_loss: 5.45195055 <2023-08-18 16:57:51.808> [INFO] Epoch#58. time_cost: 296.29 s. train_loss: 0.18923844. val_loss: 5.23308468 <2023-08-18 16:57:57.512> [INFO] Epoch#59. time_cost: 301.35 s. train_loss: 0.17699011. val_loss: 5.42624283 <2023-08-18 16:58:03.158> [INFO] Epoch#60. time_cost: 306.36 s. train_loss: 0.16317427. val_loss: 5.39616227 <2023-08-18 16:58:08.804> [INFO] Epoch#61. time_cost: 311.37 s. train_loss: 0.19332631. val_loss: 5.45374703 <2023-08-18 16:58:14.428> [INFO] Epoch#62. time_cost: 316.35 s. train_loss: 0.21091129. val_loss: 5.40493679 <2023-08-18 16:58:20.010> [INFO] Epoch#63. time_cost: 321.29 s. train_loss: 0.16297683. val_loss: 5.45400238 <2023-08-18 16:58:25.555> [INFO] Epoch#64. time_cost: 326.21 s. train_loss: 0.16020645. val_loss: 5.27163005 <2023-08-18 16:58:31.197> [INFO] Epoch#65. time_cost: 331.14 s. train_loss: 0.14810304. val_loss: 5.25995469 <2023-08-18 16:58:36.691> [INFO] Epoch#66. time_cost: 336.01 s. train_loss: 0.16113032. val_loss: 5.52074027 <2023-08-18 16:58:42.176> [INFO] Epoch#67. time_cost: 340.87 s. train_loss: 0.15936315. val_loss: 5.49150276 <2023-08-18 16:58:47.723> [INFO] Epoch#68. time_cost: 345.82 s. train_loss: 0.15403310. val_loss: 5.33653975 <2023-08-18 16:58:53.258> [INFO] Epoch#69. time_cost: 350.73 s. train_loss: 0.13249908. val_loss: 5.39583731 <2023-08-18 16:58:58.856> [INFO] Epoch#70. time_cost: 355.70 s. train_loss: 0.13025107. val_loss: 5.31142569 <2023-08-18 16:59:04.342> [INFO] Epoch#71. time_cost: 360.57 s. train_loss: 0.12579903. val_loss: 5.47937417 <2023-08-18 16:59:09.908> [INFO] Epoch#72. time_cost: 365.51 s. train_loss: 0.11129420. val_loss: 5.40961981 <2023-08-18 16:59:15.418> [INFO] Epoch#73. time_cost: 370.42 s. train_loss: 0.12030931. val_loss: 5.46381927 <2023-08-18 16:59:21.051> [INFO] Epoch#74. time_cost: 375.42 s. train_loss: 0.12002229. val_loss: 5.33053684 <2023-08-18 16:59:26.585> [INFO] Epoch#75. time_cost: 380.34 s. train_loss: 0.14545687. val_loss: 5.56017542 <2023-08-18 16:59:32.247> [INFO] Epoch#76. time_cost: 385.28 s. train_loss: 0.11673824. val_loss: 5.28624845 <2023-08-18 16:59:37.766> [INFO] Epoch#77. time_cost: 390.18 s. train_loss: 0.10143876. val_loss: 5.39271259 <2023-08-18 16:59:43.311> [INFO] Epoch#78. time_cost: 395.10 s. train_loss: 0.11019807. val_loss: 5.31737995 <2023-08-18 16:59:48.822> [INFO] Epoch#79. time_cost: 400.02 s. train_loss: 0.09523510. val_loss: 5.51333284 <2023-08-18 16:59:54.437> [INFO] Epoch#80. time_cost: 405.01 s. train_loss: 0.09562507. val_loss: 5.39607668 <2023-08-18 16:59:59.961> [INFO] Epoch#81. time_cost: 409.91 s. train_loss: 0.09793488. val_loss: 5.46005845 <2023-08-18 17:00:05.540> [INFO] Epoch#82. time_cost: 414.87 s. train_loss: 0.09791914. val_loss: 5.41902351 <2023-08-18 17:00:11.215> [INFO] Epoch#83. time_cost: 419.93 s. train_loss: 0.11880134. val_loss: 5.33904815 <2023-08-18 17:00:16.760> [INFO] Epoch#84. time_cost: 424.86 s. train_loss: 0.09215725. val_loss: 5.45071840 <2023-08-18 17:00:22.240> [INFO] Epoch#85. time_cost: 429.71 s. train_loss: 0.08812353. val_loss: 5.31730056 <2023-08-18 17:00:27.677> [INFO] Epoch#86. time_cost: 434.55 s. train_loss: 0.08547030. val_loss: 5.34717917 <2023-08-18 17:00:33.119> [INFO] Epoch#87. time_cost: 439.39 s. train_loss: 0.09604750. val_loss: 5.15446329 <2023-08-18 17:00:39.004> [INFO] Epoch#88. time_cost: 444.35 s. train_loss: 0.07881441. val_loss: 5.36302662 <2023-08-18 17:00:44.469> [INFO] Epoch#89. time_cost: 449.20 s. train_loss: 0.10159408. val_loss: 5.35543585 <2023-08-18 17:00:50.156> [INFO] Epoch#90. time_cost: 454.19 s. train_loss: 0.07790474. val_loss: 5.45754957 <2023-08-18 17:00:55.691> [INFO] Epoch#91. time_cost: 459.11 s. train_loss: 0.08819332. val_loss: 5.41740751 <2023-08-18 17:01:01.184> [INFO] Epoch#92. time_cost: 463.99 s. train_loss: 0.07858732. val_loss: 5.43756819 <2023-08-18 17:01:06.740> [INFO] Epoch#93. time_cost: 468.93 s. train_loss: 0.08139104. val_loss: 5.48051214 <2023-08-18 17:01:12.295> [INFO] Epoch#94. time_cost: 473.87 s. train_loss: 0.07225644. val_loss: 5.27634001 <2023-08-18 17:01:17.880> [INFO] Epoch#95. time_cost: 478.86 s. train_loss: 0.07885717. val_loss: 5.18226361 <2023-08-18 17:01:23.371> [INFO] Epoch#96. time_cost: 483.72 s. train_loss: 0.06458863. val_loss: 5.64266992 <2023-08-18 17:01:29.011> [INFO] Epoch#97. time_cost: 488.74 s. train_loss: 0.07287594. val_loss: 5.45775628 <2023-08-18 17:01:34.560> [INFO] Epoch#98. time_cost: 493.68 s. train_loss: 0.08625005. val_loss: 5.48485804 <2023-08-18 17:01:40.021> [INFO] Epoch#99. time_cost: 498.53 s. train_loss: 0.06330816. val_loss: 5.21741295 <2023-08-18 17:01:45.493> [INFO] Epoch#100. time_cost: 503.40 s. train_loss: 0.06806743. val_loss: 5.43132496 <2023-08-18 17:01:51.108> [INFO] Epoch#101. time_cost: 508.34 s. train_loss: 0.06728531. val_loss: 5.22270417 <2023-08-18 17:01:56.643> [INFO] Epoch#102. time_cost: 513.26 s. train_loss: 0.07651205. val_loss: 5.60741663 <2023-08-18 17:02:02.148> [INFO] Epoch#103. time_cost: 518.15 s. train_loss: 0.06601417. val_loss: 5.32008457 <2023-08-18 17:02:07.644> [INFO] Epoch#104. time_cost: 523.01 s. train_loss: 0.06601928. val_loss: 5.35806060 <2023-08-18 17:02:13.296> [INFO] Epoch#105. time_cost: 528.05 s. train_loss: 0.06467549. val_loss: 5.35822177 <2023-08-18 17:02:18.809> [INFO] Epoch#106. time_cost: 532.95 s. train_loss: 0.07210446. val_loss: 5.37992096 <2023-08-18 17:02:24.311> [INFO] Epoch#107. time_cost: 537.84 s. train_loss: 0.06643014. val_loss: 5.42277884 <2023-08-18 17:02:29.885> [INFO] Epoch#108. time_cost: 542.81 s. train_loss: 0.05864677. val_loss: 5.73795843 <2023-08-18 17:02:35.345> [INFO] Epoch#109. time_cost: 547.65 s. train_loss: 0.06491178. val_loss: 5.45618892 <2023-08-18 17:02:40.759> [INFO] Epoch#110. time_cost: 552.44 s. train_loss: 0.05744175. val_loss: 5.30905938 <2023-08-18 17:02:46.252> [INFO] Epoch#111. time_cost: 557.33 s. train_loss: 0.05695161. val_loss: 5.29266262 <2023-08-18 17:02:51.874> [INFO] Epoch#112. time_cost: 562.23 s. train_loss: 0.05810733. val_loss: 5.36781240 <2023-08-18 17:02:57.450> [INFO] Epoch#113. time_cost: 567.18 s. train_loss: 0.06044777. val_loss: 5.46884584 <2023-08-18 17:03:02.980> [INFO] Epoch#114. time_cost: 572.10 s. train_loss: 0.05924652. val_loss: 5.33167601 <2023-08-18 17:03:08.519> [INFO] Epoch#115. time_cost: 577.01 s. train_loss: 0.05411220. val_loss: 5.51172185 <2023-08-18 17:03:14.046> [INFO] Epoch#116. time_cost: 581.93 s. train_loss: 0.05188428. val_loss: 5.42046356 <2023-08-18 17:03:19.544> [INFO] Epoch#117. time_cost: 586.83 s. train_loss: 0.05135338. val_loss: 5.55253291 <2023-08-18 17:03:25.127> [INFO] Epoch#118. time_cost: 591.77 s. train_loss: 0.05763117. val_loss: 5.43751192 <2023-08-18 17:03:30.580> [INFO] Epoch#119. time_cost: 596.62 s. train_loss: 0.05355322. val_loss: 5.45424414 <2023-08-18 17:03:36.110> [INFO] Epoch#120. time_cost: 601.52 s. train_loss: 0.04620792. val_loss: 5.34147072 <2023-08-18 17:03:41.649> [INFO] Epoch#121. time_cost: 606.44 s. train_loss: 0.04430308. val_loss: 5.37313151 <2023-08-18 17:03:47.080> [INFO] Epoch#122. time_cost: 611.25 s. train_loss: 0.05001135. val_loss: 5.42952180 <2023-08-18 17:03:52.746> [INFO] Epoch#123. time_cost: 616.30 s. train_loss: 0.05492518. val_loss: 5.32263398 <2023-08-18 17:03:58.400> [INFO] Epoch#124. time_cost: 621.26 s. train_loss: 0.04963074. val_loss: 5.21857309 <2023-08-18 17:04:03.965> [INFO] Epoch#125. time_cost: 626.20 s. train_loss: 0.05894958. val_loss: 5.35917640 <2023-08-18 17:04:09.444> [INFO] Epoch#126. time_cost: 631.10 s. train_loss: 0.04274863. val_loss: 5.38477874 <2023-08-18 17:04:14.867> [INFO] Epoch#127. time_cost: 635.93 s. train_loss: 0.05253102. val_loss: 5.48755050 <2023-08-18 17:04:20.483> [INFO] Epoch#128. time_cost: 640.95 s. train_loss: 0.04418161. val_loss: 5.36615944 <2023-08-18 17:04:25.961> [INFO] Epoch#129. time_cost: 645.82 s. train_loss: 0.05168360. val_loss: 5.40945005 <2023-08-18 17:04:31.403> [INFO] Epoch#130. time_cost: 650.65 s. train_loss: 0.04251063. val_loss: 5.39642477 <2023-08-18 17:04:36.908> [INFO] Epoch#131. time_cost: 655.55 s. train_loss: 0.04522851. val_loss: 5.38135076 <2023-08-18 17:04:42.498> [INFO] Epoch#132. time_cost: 660.52 s. train_loss: 0.04688337. val_loss: 5.53917551 <2023-08-18 17:04:48.012> [INFO] Epoch#133. time_cost: 665.43 s. train_loss: 0.04650050. val_loss: 5.37437010 <2023-08-18 17:04:53.596> [INFO] Epoch#134. time_cost: 670.34 s. train_loss: 0.04531294. val_loss: 5.32696652 <2023-08-18 17:04:59.098> [INFO] Epoch#135. time_cost: 675.23 s. train_loss: 0.04147261. val_loss: 5.20313597 <2023-08-18 17:05:04.667> [INFO] Epoch#136. time_cost: 680.18 s. train_loss: 0.04341542. val_loss: 5.24532628 <2023-08-18 17:05:10.237> [INFO] Epoch#137. time_cost: 685.15 s. train_loss: 0.04142874. val_loss: 5.45382905 <2023-08-18 17:05:15.721> [INFO] Epoch#138. time_cost: 690.01 s. train_loss: 0.04173257. val_loss: 5.47300363 <2023-08-18 17:05:21.211> [INFO] Epoch#139. time_cost: 694.90 s. train_loss: 0.04357434. val_loss: 5.33823633 <2023-08-18 17:05:26.749> [INFO] Epoch#140. time_cost: 699.83 s. train_loss: 0.04733616. val_loss: 5.46602774 <2023-08-18 17:05:32.240> [INFO] Epoch#141. time_cost: 704.71 s. train_loss: 0.04136438. val_loss: 5.32237291 <2023-08-18 17:05:37.721> [INFO] Epoch#142. time_cost: 709.58 s. train_loss: 0.03627620. val_loss: 5.42067575 <2023-08-18 17:05:43.188> [INFO] Epoch#143. time_cost: 714.45 s. train_loss: 0.03925915. val_loss: 5.46165037 <2023-08-18 17:05:48.682> [INFO] Epoch#144. time_cost: 719.34 s. train_loss: 0.03792857. val_loss: 5.46521688 <2023-08-18 17:05:54.215> [INFO] Epoch#145. time_cost: 724.17 s. train_loss: 0.03960869. val_loss: 5.41369390 <2023-08-18 17:05:59.723> [INFO] Epoch#146. time_cost: 729.06 s. train_loss: 0.03514957. val_loss: 5.48138380 <2023-08-18 17:06:05.231> [INFO] Epoch#147. time_cost: 733.95 s. train_loss: 0.04448471. val_loss: 5.33512712 <2023-08-18 17:06:10.745> [INFO] Epoch#148. time_cost: 738.85 s. train_loss: 0.03458080. val_loss: 5.19379210 <2023-08-18 17:06:16.220> [INFO] Epoch#149. time_cost: 743.70 s. train_loss: 0.03476699. val_loss: 5.42594194 <2023-08-18 17:06:21.759> [INFO] Epoch#150. time_cost: 748.64 s. train_loss: 0.03833890. val_loss: 5.37024045 <2023-08-18 17:06:27.295> [INFO] Epoch#151. time_cost: 753.54 s. train_loss: 0.03714462. val_loss: 5.34531856 <2023-08-18 17:06:32.944> [INFO] Epoch#152. time_cost: 758.58 s. train_loss: 0.04011831. val_loss: 5.32632899 <2023-08-18 17:06:38.455> [INFO] Epoch#153. time_cost: 763.46 s. train_loss: 0.04507950. val_loss: 5.28544354 <2023-08-18 17:06:44.056> [INFO] Epoch#154. time_cost: 768.42 s. train_loss: 0.03567235. val_loss: 5.37078619 <2023-08-18 17:06:49.593> [INFO] Epoch#155. time_cost: 773.35 s. train_loss: 0.03932706. val_loss: 5.34415340 <2023-08-18 17:06:55.210> [INFO] Epoch#156. time_cost: 778.32 s. train_loss: 0.03472318. val_loss: 5.40771365 <2023-08-18 17:07:00.810> [INFO] Epoch#157. time_cost: 783.23 s. train_loss: 0.03815113. val_loss: 5.60919714 <2023-08-18 17:07:06.332> [INFO] Epoch#158. time_cost: 788.14 s. train_loss: 0.03213267. val_loss: 5.38632631 <2023-08-18 17:07:11.789> [INFO] Epoch#159. time_cost: 792.99 s. train_loss: 0.03157291. val_loss: 5.62310457 <2023-08-18 17:07:17.197> [INFO] Epoch#160. time_cost: 797.84 s. train_loss: 0.03437347. val_loss: 5.31411767 <2023-08-18 17:07:22.710> [INFO] Epoch#161. time_cost: 802.76 s. train_loss: 0.03174445. val_loss: 5.53535295 <2023-08-18 17:07:28.216> [INFO] Epoch#162. time_cost: 807.64 s. train_loss: 0.03642854. val_loss: 5.44857097 <2023-08-18 17:07:33.753> [INFO] Epoch#163. time_cost: 812.56 s. train_loss: 0.03894210. val_loss: 5.47438121 <2023-08-18 17:07:39.311> [INFO] Epoch#164. time_cost: 817.51 s. train_loss: 0.03483183. val_loss: 5.35244107 <2023-08-18 17:07:44.898> [INFO] Epoch#165. time_cost: 822.46 s. train_loss: 0.03730110. val_loss: 5.42034888 <2023-08-18 17:07:50.396> [INFO] Epoch#166. time_cost: 827.34 s. train_loss: 0.03387760. val_loss: 5.33185983 <2023-08-18 17:07:56.042> [INFO] Epoch#167. time_cost: 832.29 s. train_loss: 0.03823958. val_loss: 5.32589149 <2023-08-18 17:08:01.592> [INFO] Epoch#168. time_cost: 837.25 s. train_loss: 0.03560706. val_loss: 5.39625525 <2023-08-18 17:08:07.178> [INFO] Epoch#169. time_cost: 842.21 s. train_loss: 0.02904032. val_loss: 5.35998750 <2023-08-18 17:08:12.667> [INFO] Epoch#170. time_cost: 847.10 s. train_loss: 0.03341103. val_loss: 5.49449563 <2023-08-18 17:08:18.180> [INFO] Epoch#171. time_cost: 852.01 s. train_loss: 0.03474875. val_loss: 5.48542976 <2023-08-18 17:08:23.746> [INFO] Epoch#172. time_cost: 856.94 s. train_loss: 0.03299613. val_loss: 5.52249503 <2023-08-18 17:08:29.276> [INFO] Epoch#173. time_cost: 861.83 s. train_loss: 0.03398239. val_loss: 5.63685536 <2023-08-18 17:08:34.753> [INFO] Epoch#174. time_cost: 866.69 s. train_loss: 0.02945490. val_loss: 5.42094088 <2023-08-18 17:08:40.240> [INFO] Epoch#175. time_cost: 871.56 s. train_loss: 0.02840251. val_loss: 5.69379497 <2023-08-18 17:08:45.826> [INFO] Epoch#176. time_cost: 876.53 s. train_loss: 0.02756416. val_loss: 5.56099296 <2023-08-18 17:08:51.384> [INFO] Epoch#177. time_cost: 881.48 s. train_loss: 0.03283496. val_loss: 5.40805626 <2023-08-18 17:08:57.047> [INFO] Epoch#178. time_cost: 886.44 s. train_loss: 0.03240528. val_loss: 5.31137824 <2023-08-18 17:09:02.554> [INFO] Epoch#179. time_cost: 891.30 s. train_loss: 0.02928356. val_loss: 5.42326951 <2023-08-18 17:09:08.079> [INFO] Epoch#180. time_cost: 896.20 s. train_loss: 0.03587892. val_loss: 5.45484829 <2023-08-18 17:09:13.542> [INFO] Epoch#181. time_cost: 901.07 s. train_loss: 0.04012054. val_loss: 5.44649816 <2023-08-18 17:09:19.104> [INFO] Epoch#182. time_cost: 906.01 s. train_loss: 0.02857378. val_loss: 5.39741778 <2023-08-18 17:09:24.655> [INFO] Epoch#183. time_cost: 910.94 s. train_loss: 0.02655693. val_loss: 5.41928601 <2023-08-18 17:09:30.200> [INFO] Epoch#184. time_cost: 915.86 s. train_loss: 0.03125123. val_loss: 5.58228779 <2023-08-18 17:09:35.700> [INFO] Epoch#185. time_cost: 920.73 s. train_loss: 0.03121312. val_loss: 5.44002223 <2023-08-18 17:09:41.265> [INFO] Epoch#186. time_cost: 925.68 s. train_loss: 0.02763961. val_loss: 5.45899653 <2023-08-18 17:09:46.883> [INFO] Epoch#187. time_cost: 930.69 s. train_loss: 0.02685601. val_loss: 5.48374534 <2023-08-18 17:09:52.440> [INFO] Epoch#188. time_cost: 935.61 s. train_loss: 0.02776013. val_loss: 5.46111584 <2023-08-18 17:09:57.927> [INFO] Epoch#189. time_cost: 940.50 s. train_loss: 0.02638166. val_loss: 5.60892177 <2023-08-18 17:10:03.526> [INFO] Epoch#190. time_cost: 945.49 s. train_loss: 0.02653074. val_loss: 5.13279104 <2023-08-18 17:10:09.458> [INFO] Epoch#191. time_cost: 950.45 s. train_loss: 0.02427226. val_loss: 5.58440328 <2023-08-18 17:10:14.906> [INFO] Epoch#192. time_cost: 955.29 s. train_loss: 0.03171158. val_loss: 5.61189842 <2023-08-18 17:10:20.516> [INFO] Epoch#193. time_cost: 960.28 s. train_loss: 0.02493854. val_loss: 5.39307165 <2023-08-18 17:10:26.003> [INFO] Epoch#194. time_cost: 965.17 s. train_loss: 0.02884255. val_loss: 5.54699516 <2023-08-18 17:10:31.489> [INFO] Epoch#195. time_cost: 970.02 s. train_loss: 0.03158595. val_loss: 5.52103448 <2023-08-18 17:10:36.968> [INFO] Epoch#196. time_cost: 974.90 s. train_loss: 0.02557721. val_loss: 5.46148872 <2023-08-18 17:10:42.548> [INFO] Epoch#197. time_cost: 979.86 s. train_loss: 0.02455417. val_loss: 5.64398980 <2023-08-18 17:10:48.120> [INFO] Epoch#198. time_cost: 984.81 s. train_loss: 0.02883675. val_loss: 5.24348927 <2023-08-18 17:10:53.642> [INFO] Epoch#199. time_cost: 989.71 s. train_loss: 0.02697669. val_loss: 5.49870038 <2023-08-18 17:10:59.159> [INFO] Epoch#200. time_cost: 994.59 s. train_loss: 0.02243770. val_loss: 5.45685840 <2023-08-18 17:11:04.705> [INFO] Epoch#201. time_cost: 999.52 s. train_loss: 0.02589454. val_loss: 5.39851737 <2023-08-18 17:11:10.261> [INFO] Epoch#202. time_cost: 1004.47 s. train_loss: 0.02893260. val_loss: 5.52150702 <2023-08-18 17:11:15.859> [INFO] Epoch#203. time_cost: 1009.46 s. train_loss: 0.02829824. val_loss: 5.46824288 <2023-08-18 17:11:21.350> [INFO] Epoch#204. time_cost: 1014.35 s. train_loss: 0.02839780. val_loss: 5.33534241 <2023-08-18 17:11:26.858> [INFO] Epoch#205. time_cost: 1019.22 s. train_loss: 0.02283491. val_loss: 5.48758030 <2023-08-18 17:11:32.283> [INFO] Epoch#206. time_cost: 1024.01 s. train_loss: 0.02938521. val_loss: 5.45340896 <2023-08-18 17:11:37.778> [INFO] Epoch#207. time_cost: 1028.89 s. train_loss: 0.02286907. val_loss: 5.38048625 <2023-08-18 17:11:43.357> [INFO] Epoch#208. time_cost: 1033.86 s. train_loss: 0.02554865. val_loss: 5.55267978 <2023-08-18 17:11:48.878> [INFO] Epoch#209. time_cost: 1038.78 s. train_loss: 0.02529276. val_loss: 5.46401834 <2023-08-18 17:11:54.392> [INFO] Epoch#210. time_cost: 1043.68 s. train_loss: 0.02222722. val_loss: 5.41124010 <2023-08-18 17:11:59.876> [INFO] Epoch#211. time_cost: 1048.55 s. train_loss: 0.02464435. val_loss: 5.40487456 <2023-08-18 17:12:05.365> [INFO] Epoch#212. time_cost: 1053.39 s. train_loss: 0.02601153. val_loss: 5.42090082 <2023-08-18 17:12:10.882> [INFO] Epoch#213. time_cost: 1058.29 s. train_loss: 0.02078980. val_loss: 5.40114021 <2023-08-18 17:12:16.442> [INFO] Epoch#214. time_cost: 1063.26 s. train_loss: 0.02538042. val_loss: 5.50385857 <2023-08-18 17:12:21.926> [INFO] Epoch#215. time_cost: 1068.14 s. train_loss: 0.02432403. val_loss: 5.45867205 <2023-08-18 17:12:27.432> [INFO] Epoch#216. time_cost: 1073.02 s. train_loss: 0.02621710. val_loss: 5.62054467 <2023-08-18 17:12:32.867> [INFO] Epoch#217. time_cost: 1077.86 s. train_loss: 0.02467208. val_loss: 5.46568441 <2023-08-18 17:12:38.490> [INFO] Epoch#218. time_cost: 1082.77 s. train_loss: 0.02607746. val_loss: 5.49830675 <2023-08-18 17:12:44.123> [INFO] Epoch#219. time_cost: 1087.77 s. train_loss: 0.02776598. val_loss: 5.42323661 <2023-08-18 17:12:49.695> [INFO] Epoch#220. time_cost: 1092.72 s. train_loss: 0.02273856. val_loss: 5.42024589 <2023-08-18 17:12:55.296> [INFO] Epoch#221. time_cost: 1097.74 s. train_loss: 0.02539457. val_loss: 5.26896286 <2023-08-18 17:13:00.879> [INFO] Epoch#222. time_cost: 1102.70 s. train_loss: 0.02268215. val_loss: 5.46498084 <2023-08-18 17:13:06.403> [INFO] Epoch#223. time_cost: 1107.60 s. train_loss: 0.02315321. val_loss: 5.50621009 <2023-08-18 17:13:11.997> [INFO] Epoch#224. time_cost: 1112.57 s. train_loss: 0.02258990. val_loss: 5.49236298 <2023-08-18 17:13:17.514> [INFO] Epoch#225. time_cost: 1117.48 s. train_loss: 0.02169418. val_loss: 5.61205339 <2023-08-18 17:13:22.977> [INFO] Epoch#226. time_cost: 1122.33 s. train_loss: 0.02059807. val_loss: 5.51667738 <2023-08-18 17:13:28.632> [INFO] Epoch#227. time_cost: 1127.37 s. train_loss: 0.02105716. val_loss: 5.38954735 <2023-08-18 17:13:34.130> [INFO] Epoch#228. time_cost: 1132.24 s. train_loss: 0.02462164. val_loss: 5.44811368 <2023-08-18 17:13:39.678> [INFO] Epoch#229. time_cost: 1137.15 s. train_loss: 0.02404069. val_loss: 5.41940117 <2023-08-18 17:13:45.329> [INFO] Epoch#230. time_cost: 1142.18 s. train_loss: 0.02199012. val_loss: 5.49373794 <2023-08-18 17:13:50.942> [INFO] Epoch#231. time_cost: 1147.18 s. train_loss: 0.02008617. val_loss: 5.40747380 <2023-08-18 17:13:56.539> [INFO] Epoch#232. time_cost: 1152.15 s. train_loss: 0.01896449. val_loss: 5.38771868 <2023-08-18 17:14:02.081> [INFO] Epoch#233. time_cost: 1157.07 s. train_loss: 0.02353778. val_loss: 5.31932235 <2023-08-18 17:14:07.676> [INFO] Epoch#234. time_cost: 1162.06 s. train_loss: 0.02101104. val_loss: 5.33468461

`import argparse
from pathlib import Path
import torch
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from rotate_captcha_crack.common import device
from rotate_captcha_crack.dataset import ImgTsSeqFromPath, RotDataset, from_google_streetview, from_pcgs_pics
from rotate_captcha_crack.helper import default_num_workers
from rotate_captcha_crack.lr import LRManager
from rotate_captcha_crack.model import RotNetR
from rotate_captcha_crack.trainer import Trainer
from rotate_captcha_crack.utils import slice_from_range
from rotate_captcha_crack.visualizer import visualize_train
if name == "main":
parser = argparse.ArgumentParser()
parser.add_argument(
"--resume", "-r", type=int, default=None, help="Resume from which index. -1 leads to the last training process"
)
opts = parser.parse_args()
#################################
### Custom configuration area ###
dataset_root = Path("test/1C Lincoln Cent (Wheat Reverse)-obverse/")

# img_paths = from_google_streetview(dataset_root)
img_paths = from_pcgs_pics(dataset_root)
# print(img_paths[0])
cls_num = 180
train_img_paths = slice_from_range(img_paths, (0.0, 0.70))
train_dataset = RotDataset(ImgTsSeqFromPath(train_img_paths), cls_num=cls_num)
val_img_paths = slice_from_range(img_paths, (0.70, 1.0))
for v in val_img_paths:
    print(v)
val_dataset = RotDataset(ImgTsSeqFromPath(val_img_paths), cls_num=cls_num)

num_workers = default_num_workers()
train_dataloader = DataLoader(
    train_dataset,
    batch_size=32,
    num_workers=num_workers,
    shuffle=True,
    drop_last=True,
)
val_dataloader = DataLoader(
    val_dataset,
    batch_size=32,
    num_workers=num_workers,
    drop_last=True,
    shuffle=True,
)

model = RotNetR(cls_num)
model = model.to(device)

lr = 0.01
momentum = 0.9
epochs = 300
steps = 128
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
    optimizer, max_lr=lr, pct_start=0.25, epochs=epochs, steps_per_epoch=steps
)
lr = LRManager(lr, scheduler, optimizer)
loss = CrossEntropyLoss()

trainer = Trainer(model, train_dataloader, val_dataloader, lr, loss, epochs, steps)
### Custom configuration area ###
#################################

if opts.resume is not None:
    trainer.resume(opts.resume)

trainer.train()

visualize_train(trainer.finder.model_dir)

`

我刚刚看到你的训练时间才意识到应该是数据量太少再加上纹理类型也很少（就两种）的问题，如果你的应用场景就是特定的几个图样那不如直接用传统的特征点匹配

添加了50000张不同钱币图片用于训练，但是loss还是只有0.6降不下去了。传统的特征点匹配的方法已经尝试了，因为每个钱币色泽和新旧磨损不一样，所以并不好用

改成RotNet LOSS就下来了

莫非是因为分类数变多了

authors: Starry-OvO hacktmz

repo: Starry-OvO/rotate-captcha-crack

license: Unlicense


text:
[feat]: Install on existing system?

Prerequisites

[X] I have searched for existing issues that already suggest this feature, without success.

Describe the Feature Request
I would really like to install EmbassyOS on a Ubuntu or Debian server, so without the OS.
Is there a technical reason why this is not possible today?
Describe the Use Case
It would allow for a larger userbase
Describe Preferred Solution
Install script
Describe Alternatives
No response
Anything else?
No response

We haven't tested it and certain features were developed with the assumption of the environment it runs in (GPIO for speaker feedback, WiFi exists and is managed by NMCLI). We are working to expand hardware so we will be sorting this out sooner than later. If you'd like you can try installing the binaries produced by the build process to the server and see where things go wrong.

Completed: https://github.com/Start9Labs/embassy-os-deb

Run in a VM instead if you don't want to provision separate hardware

source: gharchive/issue

authors: ProofOfKeags chrisguida elvece kroese

license: MIT

license_type: permissive

license_source: github-api


text:
backend: sdk init: output file location. fixes #1854

start-sdk init used to run completely silent. Now we are showing the current/generated developer.key.pem based on ticket https://github.com/Start9Labs/start-os/issues/1854

sample run:
➜  backend git:(sdk_init_message-fixes1854) ✗ start-sdk init
New developer key generated at /Users/jadi/.embassy/developer.key.pem
➜  backend git:(sdk_init_message-fixes1854) ✗ start-sdk init
Developer key already exists at /Users/jadi/.embassy/developer.key.pem

Resent this PR, signed.

source: gharchive/pull-request


text:
.table margin-bottom: 1rem; breaks bordered responsive tables

with this HTML

      ....
....

Because the .table margin bottom, there is a space between the table and the border from the responsive table which breaks the UI.
The issue comes because table-light, to have a white bg table, makes the borders also white, but if you want to add borders and there is a .table-responsive containing the table, the table-bordered should be added to the .table-responsive, because there is a BT rule to make border: 0 for .table-responsive > .table-bordered.
Lots of coincidences :)
Workaround: I removed the margin bottom from the table adding that to my CSS.

I will take a look at this and bring it into consideration when doing the new build that is coming soon. Thanks!

authors: davidtmiller ppazos


text:
Added method to read distance

Added method to read distance

@nu1 thank you for a pull request! I'll add it tomorrow. Thanks a lot!

repo: StasDoskalenko/react-native-google-fit

license: mit

license_source: bigquery


text:
Exclude docs dir from linting

Ruff currently complains about docs/conf.py, but since this is auto-generated we should probably ignore it.
I ran into this while trying to use the template today. Alternatives could include loosening the set of rules we enforce or having more fine-grained exclusions under the docs folder.

Does it make sense to target only the conf.py file instead of the whole docs directory? Typically, conf.py is the only Python file in there so maybe not important?

Good question. Let me test to see if targeting just that file works, and if so this PR can be more surgical.

authors: ToucheSir goatsweater

repo: StatCan/datascience-cookiecutter


text:
Vector length

added a function to return the length of a vector

This should be merged into vector_spaces instead I think.

Will fix that for all the stuff then


text:
Add Smacker Video

SteamDB app page links to a few games using this
https://steamdb.info/app/215930
https://steamdb.info/app/549170
https://steamdb.info/app/410980
Brief explanation of the change
closes #255
Added a rule for Smacker Video

Looks good to me now, thanks for the PR!

authors: Lifeismana diedyesterdaywashere

repo: SteamDatabase/FileDetectionRuleSets


text:
With 3.0.0 version of Steeltoe getting Socket Exception Error

Hi
with latest version of Steeltoe , we are getting connection refused error. It was wroking fine previous version of Steeltoe .
We are using linux container for pusing this to cloud. Below are the exception details.
An unhandled Mvc exception was encountered.","Exception":"System.Net.Http.HttpRequestException: Connection refused\n --->
System.Net.Sockets.SocketException (111): Connection refused\n at System.Net.Http.ConnectHelper.ConnectAsync(String host, Int32 port,
CancellationToken cancellationToken)\n --- End of inner exception stack trace ---\n at System.Net.Http.ConnectHelper.ConnectAsync(String host,
Int32 port, CancellationToken cancellationToken)\n at System.Net.Http.HttpConnectionPool.ConnectAsync(HttpRequestMessage request, Boolean allowHttp2,
CancellationToken cancellationToken)\n at System.Net.Http.HttpConnectionPool.CreateHttp11ConnectionAsync(HttpRequestMessage request, CancellationToken
cancellationToken)\n at System.Net.Http.HttpConnectionPool.GetHttpConnectionAsync(HttpRequestMessage request, CancellationToken cancellationToken)\n at
System.Net.Http.HttpConnectionPool.SendWithRetryAsync(HttpRequestMessage request, Boolean doRequestAuth, CancellationToken cancellationToken)\n
at System.Net.Http.RedirectHandler.SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)\n at
System.Net.Http.DiagnosticsHandler.SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)\n at
Microsoft.Extensions.Http.Logging.LoggingHttpMessageHandler.SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)\n
at Steeltoe.Common.Http.Discovery.DiscoveryHttpMessageHandler.SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)\n at
Microsoft.Extensions.Http.Logging.LoggingScopeHttpMessageHandler.SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)\n
at System.Net.Http.HttpClient.FinishSendAsyncBuffered(Task`1 sendTask, HttpRequestMessage request, CancellationTokenSource cts, Boolean disposeCts)\n at

I already have added Steeltoe.Connector.CloudFoundry.

Program.cs looks like as
.UseCloudHosting()
.AddCloudFoundryActuators()
.AddCloudFoundryConfiguration()
.AddServiceDiscovery(options => options.UseEureka())
.UseStartup();
In appsettings.json
{
"AllowedHosts": "*",
"spring": {
"application": {
"name": "vendordetail"
}
},
"eureka": {
"client": {
"shouldRegisterWithEuerka": true
},
"instance": {
"port": 5000,
"nonSecurePortEnabled": false,
"securePortEnabled": true,
"appname": "vendordetail"
}
}
}
This service is getting registered with service discovery without an issue. We are getting above issue only when some other api is trying to call and they are not not able to connect this this service and getting Connection refused 111 error.

Your settings are telling Steeltoe to register your app as listening for HTTPS connections on port 5000, which doesn't seem right for a Cloud Foundry environment. If that's not right when deployed, but is what you want locally, consider moving the settings that don't apply in the deployment environment over to appsettings.development.json

Hi Tim
I have upgraded Steeltoe versions to 3.0.2 and added some proxy settings and now my problem has been resolved. Thanks for your assistance.

authors: TimHess standardissue

repo: SteeltoeOSS/Steeltoe


text:
🛑 SteffTek.de is down

In 33d154f, SteffTek.de (https://stefftek.de/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SteffTek.de is back up in a2f2aee after 12 minutes.

repo: SteffTek/Status


text:
Hyperopt Placement Group Error

Hi I tried to run some different Search Space and getting the following Placement Group Error. Could you check this out? Thank you very much for your time
Traceback (most recent call last):
File "c:\Users\alexo\Desktop\github Copy\src\hyperopt\hyperopt_ray.py", line 414, in
hyperopt_full()
File "c:\Users\alexo\Desktop\github Copy\src\hyperopt\hyperopt_ray.py", line 407, in hyperopt_full
hyperopt_model(model, save_results, coin, time_frame)
File "c:\Users\alexo\Desktop\github Copy\src\hyperopt\hyperopt_ray.py", line 374, in hyperopt_model
hyperopt_dataset(model, coin, tf, save_results)
File "c:\Users\alexo\Desktop\github Copy\src\hyperopt\hyperopt_ray.py", line 330, in hyperopt_dataset
analysis = hyperopt(
File "c:\Users\alexo\Desktop\github Copy\src\hyperopt\hyperopt_ray.py", line 295, in hyperopt
return tuner.fit()
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\tune\tuner.py", line 377, in fit
return self._local_tuner.fit()
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\tune\impl\tuner_internal.py", line 476, in fit
analysis = self._fit_internal(trainable, param_space)
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\tune\impl\tuner_internal.py", line 592, in _fit_internal
analysis = run(
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\tune\tune.py", line 1001, in run
runner.cleanup()
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\tune\execution\tune_controller.py", line 1975, in cleanup
self._cleanup_trials()
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\tune\execution\tune_controller.py", line 808, in _cleanup_trials
self._actor_manager.cleanup()
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\air\execution_internal\actor_manager.py", line 890, in cleanup
self._resource_manager.cancel_resource_request(resource_request)
File "C:\Users\alexo\anaconda3\envs\Crypto\lib\site-packages\ray\air\execution\resources\placement_group.py", line 151, in cancel_resource_request
future = self._pg_to_staging_future.pop(pg)
KeyError: <ray.util.placement_group.PlacementGroup object at 0x000001CD09252850>

Did you check if the change in hyperparameters is in the allowed range? Could you tell me what you adjusted?

Hey sorry I wa about do write you. I had somehow a wrong version of ray now everything seems to work again. Anyway thanks for the quick response

Yeah I can send you my files I only adjust the coins (less coins) and less models. Getting the error at the beginning of random forest.  Also adjust parallel trials to 5 and Batch Size "batch_size": tune.choice([16, 32, 64, 128]).
hyperopt.zip

Hmm interesting, I thought the key error might've happened because you added something to the search space. I won't have time to look at it this week, if you need quick answers then I'd suggest asking the people that work with Ray.

Ok I just testet with the original Search space and didnt change anything else. Getting the same placement Group error unfortunately

Are you using the ray version that is in the requirements.txt? I had some issues when I tried a newer version

Yes using ray 2.4.0 just like in the requirements.txt

Yeah I can send you my files I only adjust the coins (less coins) and less models. Getting the error at the beginning of random forest. Also adjust parallel trials to 5 and Batch Size "batch_size": tune.choice([16, 32, 64, 128]).

Are you sure the .zip you included has this too? I see now changes compared to the code on GitHub

And does everything work for the other models and this only happens at random forest?


text:
🛑 DNS (he.net) is down

In 9f0e8d4, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in 7ca03c0.

repo: Sternwarte-St-Ottilien-e-V/status


text:
🛑 DNS (he.net) is down

In 6661430, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in 4d99ca4.


text:
🛑 DNS (he.net) is down

In 16064d1, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in 8d2eefa.


text:
🛑 DNS (he.net) is down

In 4998b7b, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in 92c1c1b.


text:
🛑 DNS (he.net) is down

In c3ddda8, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in 8d251c2.


text:
🛑 DNS (he.net) is down

In 6ed7ba6, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in b599b1e.


text:
🛑 DNS (he.net) is down

In 3256a7f, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in 0f9cdb6.


text:
🛑 DNS (he.net) is down

In cb60b27, DNS (he.net) ($HE_NS) was down:

HTTP code: 0
Response time: 0 ms

Resolved: DNS (he.net) is back up in a9436e3.


text:
Can't copy files to CTR NAND from SD with FBI 2.1.1

I'm on a New 3DS XL, with ARM9LoaderHax and sysNAND 10.7, along with a 64GB uSD, formatted to FAT32 with 64k clusters.
Whenever I try to copy any files from the SD card to CTR NAND, FBI refuses with the following error:
Failed to paste content.
/injector.cxi (or any other file, it doesn't matter)
Result code: 0xC8804478
Level: Status (25)
Summary: Not found (4)
Module: FS (17)
Desc: Doesn't exist / Failed to open (120)
I don't really see how the file is "not found" when it's on the root of my SD card and I can open it just fine when my CFW loads, as well as when I view the contents of the SD card on my PC.

Try updating to 2.2.1. Copying to CTR NAND works fine for me there.

Oops, I didn't notice there's a new release. I'll try it now.

That worked. Sorry for the false alarm.


text:
Black Border around virtual GB screen

I compiled the the latest source, and there's a black border around the gb screen even when stretched to the full screen.
I made sure to disable all border options, still there.

Should be fixed in latest source.


text:
[adding] dadsats.com

Beside https://github.com/StevenBlack/hosts/issues/1334
dadsats.com
is another annoying Ads domain

Done.  Thanks @beerisgood.

authors: StevenBlack beerisgood

repo: StevenBlack/hosts


text:
[Bug]: auto-redact PDF Input field missing?

Installation Method
Docker
The Problem
Hello, I noticed that the input field for the PDF is missing in auto-redact page, my installation and also the oficial page, Im wrong or is it really missing? https://stirlingpdf.io/auto-redact
Version of Stirling-PDF
0.35.1
Last Working Version of Stirling-PDF
No response
Page Where the Problem Occurred
No response
Docker Configuration
No response
Relevant Log Output
No response
Additional Information
No response
Browsers Affected
No response
No Duplicate of the Issue

[X] I have verified that there are no existing issues raised related to my problem.

Oh, looks like its already fixed in next version, sorry for the duplicated post, you can close it, thanks.

released now too!

authors: Frooodle thiagoor-cpu

repo: Stirling-Tools/Stirling-PDF


text:
[CROSSDATA-322] Performance improvements

Avoid objects creation: replace map with mapPartitions
Several minor changes

I prefer to avoid the use of ._ for the same reasons that we avoid the use of .* in Java...
The others stuff LGTM...

+1

authors: darroyocazorla luismcl pmadrigal

repo: Stratio/Crossdata


text:
Please make ztab line For pi3

This is Great job. :)
But I can understand only disk_size.
I want to get 75% phisical memory.
How do I do ?
Thank you :)
Yutayuta

or default ztab is most efficnet for pi ?

swap	alg	mem_limit	disk_size	swap_priority	page-cluster	swappiness
swap	lz4	200M		600M		75		0		90
Disk size actually means very little as its the virtual size dependent on compression which is alg and input data = resultant compression and varies greatly.
mem_limit is the actual physical mem limit so if you have 1gb you could make that 750M.
Max virtual size is always a guess but with LZ4 x3 is about approx so 750*3 = 2250M
Usually 50% 500M mem_limit and 1500M disk size is enough but you can go with the above if you wish.

Thank you so much.
I don't close this issue , because I want to leave this perfect explanation.
I got it :)
I try to change ztab disk_size 50% at first .
yutayuta

This script causes Good latency and fast :)

The biggest thing is that it actually checks for other zram services at the start.
I still struggle to believe that simple method is missing from others and those scripts will not co-exist with other zram services.
That is even the packages in mainline distro's such as ubuntu & debian.
Then they still provide no control over mem_limit and disk size and have a alg to create a device per core and divide 50% ram between them.
zram has been multistream since 3.15 and the above is totally pointless and actually a worse solution especially with big/little core arrangement.
zram-config started as a bit of a protest project of read the docs!
https://www.kernel.org/doc/Documentation/blockdev/zram.txt
Then it got expanded a bit to use zram disks to cover high write dirs and protect against sd card block wear.
You might want to create a zlog as that is also quite beneficial on a Pi.

Much thanks :)

I also use this.
https://github.com/graysky2/profile-sync-daemon
According to this , It uses tmpfs

as overlaid ram.
Now your script uses fixed size ram.
I think tmpfs is better.
How  ?

tmps should when idle push out to swap so if zram is installed then essentially it will be doing the same thing.
They only advantage is that zram-config is 100% compressed if idle or live.
I will have a look one time

authors: StuartIanNaylor yutayu

repo: StuartIanNaylor/zram-config


text:
🛑 Nextcloud Papa is down

In 05e8457, Nextcloud Papa ($NEXTCLOUD_2) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Nextcloud Papa is back up in 2a23cf5.

repo: StudFu-WordToMD/status


text:
🛑 Nextcloud Papa is down

In bc06edf, Nextcloud Papa ($NEXTCLOUD_2) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Nextcloud Papa is back up in 06b5f1b.


text:
🛑 Nextcloud Papa is down

In 1b6bb64, Nextcloud Papa ($NEXTCLOUD_2) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Nextcloud Papa is back up in d5d9847.


text:
Add visualization of ortholog alignments

A cool feature would be to (in the Orthologs window) add a button that triggers a script/webapp(?) that aligns the sequences listed (maybe checkboxes for user to choose). At worst, this is a BLAST. But surely there must be better web functions for this, like clustal...?

MUSCLE (multiple sequence alignment algorithm) has a REST web service here: http://www.ebi.ac.uk/Tools/webservices/services/msa/muscle_rest
nice little MSA viewer widget: http://msa.biojs.net/
stitching this all together might be a nice intern/rotation project...

This looks great.  I will play a few example API calls and post results here.

That's really neat (for aa sequences). For most other organisms in wikigenomes, aa alignments will be a great tool. Chlamydia is a little unique in that the DNA sequences across species are conserved, which is why we also are fond of gene alignments for orthologs.

both protein and nucleotide MSAs should be possible with MUSCLE and the biojs widget I think.  Unless you @khybiske saw something that says otherwise?

You are right. I had only clicked their 'use it' button...

I just did a nucleotide alignment:
http://www.ebi.ac.uk/Tools/services/web/toolresult.ebi?jobId=muscle-I20170714-190003-0316-2963421-oy

authors: andrewsu khybiske putmantime


text:
[beta] Inside the hover messages, the error count is the line error count

(Using SublimeLinter 4.0.0-rc.11 and SublimeLinter-contrib-eslint 1.10.0
in ST3 3156 x64 (clean installation))
Based on 4.0.0-rc.1.txt in the new version:

Hover over an error to view the message.
Hover over the gutter to view all error messages for that line.

But, currently, inside the hover message, the error count is the line error count (instead of the current position error count), which I find it very confusing.
For example. in the following code line 4 has 2 errors, and hovering hovering over the gutter shows '2 errors' as expected:

But. hovering on either error position, the count in the message is also '2 errors'
(although on each position there's only 1 error):

IMHO the '2 errors' count should appear only on the gutter - the hover message should display the error count for the current position , i.e. in this case it would be '1 error' for each position.

Jup, that's a bug. We should probably just remove the count from the region popup.

Don't we want the error type (and color) to be displayed for the inline popup as well?
Tbh I find the logic at this point kind of weird as I can't easily predict when the line_dict will have what kind of contents, so I'd rather have you work on the logic on implementing that instead of submitting a PR.

authors: FichteFoll braver darkred


text:
feat: allow retries on task profiling steps

Description
(4 first commits are refactoring to pytest)
Fixes FL-496
How has this been tested?

Checklist

[ ] changelog was updated with notable changes
[ ] documentation was updated

/e2e

End to end tests: :hourglass_flowing_sand: STARTING

/e2e

End to end tests: :hourglass_flowing_sand: STARTING

/e2e

End to end tests: :hourglass_flowing_sand: STARTING

/e2e

authors: Owlfred guilhem-barthes

repo: Substra/substra-backend


text:
chore: remove chaincode on codebase

Description
close FL-1349
Checklist

[ ] changelog was updated with notable changes
[ ] documentation was updated

/e2e --tests sdk

End to end tests: :hourglass_flowing_sand: STARTING

/e2e --tests sdk

End to end tests: :hourglass_flowing_sand: STARTING

Looks fine to me, but others are better at backending

Maybe @guilhem-barthes you can have a look ? 🙏

authors: Owlfred ThibaultFy


text:
feat: add editable mode env variable configuration

Summary
Add the possibility to force the Dependency editable_mode through the environment variable SUBSTRA_FORCE_EDITABLE_MODE
Notes
Please check if the PR fulfills these requirements

[x] If the feature has an impact on the user experience, the changelog has been updated
[x] Tests for the changes have been added (for bug fixes / features)
[x] Docs have been added / updated (for bug fixes / features)
[x] The commit message follows the conventional commit specification

To be seen in which order we want to merge everything to minimise merge conflicts ^^

You tell me ! :)

repo: Substra/substrafl


text:
The ALRecipe web data extraction has an error and won't complete

Compling with the instructions to only type input into one of the two fields results in the error, as does typing into both. used real values as well.
https://apps-dev.suffolklitlab.org/interview?i=docassemble.ALRecipes:data/questions/web_data_extraction.yml#page1

Looks like the domain is gone. New domain seems to be: https://jdi.coalitionmanager.org/contactmanager/contact/publicdirectory
probably best just to remove this example

authors: aidanielson nonprofittechy

repo: SuffolkLITLab/docassemble-ALRecipes


text:
Update this interview repository

[ ] Update this repo description using the following template:
A Docassemble interview for the [JURISDICTION] [FORM TITLE]
[ ] If this interview is live, add the URL where it can be found. If not, create a new issue to discuss what needs to happen with it.
[ ] Add a topic tag for the jurisdiction (i.e., Massachusetts)
[ ] Add interview-upkeep.yml workflow file to .github/workflows and commit it.
[ ] Update the README introduction using the following template:
This is a Docassemble interview to complete the [JURISDICTION] [FORMS] forms online. The live interview can be found at [URL].

Since this interview is not live, I don't think it makes sene to do any updates. They can happen as part of any further development.


text:
Close #252, alcourts tests MVP

Also does some transformation of the address if needed since it can come from user input. I can change that back if desired.
Having multiple pages this way ends up letting the test run faster. When we have a testing step for tapping the 'Back' button, we can get rid of some of these pages.
Close #252

Something to keep in mind with this test, which I recognize isn't complete yet, is that the user input of name and address will almost always be a substitute for choosing from a list and vice versa. If the user provided the name and address of a court, they shouldn't interact with the short_description() etc. methods. Conversely, if they do select from a list, the address should generally be provided in the spreadsheet or database.

Something to keep in mind with this test, which I recognize isn't complete yet, is that the user input of name and address will almost always be a substitute for choosing from a list and vice versa. If the user provided the name and address of a court, they shouldn't interact with the short_description() etc. methods. Conversely, if they do select from a list, the address should generally be provided in the spreadsheet or database.

I'm not sure what this means in practice. That is, how should the code and tests be different?
Should the code and tests assume that the user will not be inputting an address? That is, that the input doesn't need to be cleaned up?
What's the relevance to the description? The tests just define the description so that there's a way to test that short_description() functions correctly.
Also, should I take this to mean that eventually the tests should use JSON court data? If so, whoever writes the tests and data for that needs to understand the different edge cases that need to be tested.

Something to keep in mind with this test, which I recognize isn't complete yet, is that the user input of name and address will almost always be a substitute for choosing from a list and vice versa. If the user provided the name and address of a court, they shouldn't interact with the short_description() etc. methods. Conversely, if they do select from a list, the address should generally be provided in the spreadsheet or database.

I'm not sure what this means in practice. That is, how should the code and tests be different?
Should the code and tests assume that the user will not be inputting an address? That is, that the input doesn't need to be cleaned up?

We can happily test all of the methods. My comment was about what changes to the methods are justified.
These methods: short_label, short_label_and_address, and short_description are all meant to be used when the developer provides a list of courts and the user needs to choose one of the items in the list. Therefore there is no need to make any of these three methods handle user input.
That said, I think the change to make short_label not depend on the city is justified, because the developer might upload a list of courts without addresses. But it does not make sense to remove the address from the short_label_and_address method.
FYI, these were all copied blindly from the MACourt class, and actually they don't look like they'll ever be used in the ALCourtLoader/ALCourt class, because that class does not load all of the court data until a court is selected from the list. So maybe we should just remove the methods for now! The functionality they have should be migrated to the ALCourtLoader class.

What's the relevance to the description? The tests just define the description so that there's a way to test that short_description() functions correctly.

See above--it was just one of the 3 methods that doesn't really need to handle user inputted court information.

Also, should I take this to mean that eventually the tests should use JSON court data? If so, whoever writes the tests and data for that needs to understand the different edge cases that need to be tested.

Eventually we should test the data loading functionality. There is a sample XLSX court file in the suffolklitlab/docassemble-ALGenericJurisdiction repo.
This class is being used by one developer in Canada right now, and I think we still have a lot to learn about how it can be built out in a generic way. I don't remember if either Illinois or Louisiana are using it yet--I think they are both working with just selecting the user's county name. I'm willing to wait to see what features are needed before we add any functionality.

Made changes. Not sure if this is what was desired. For example, if those methods are being removed, I'm not sure what this is meant to be testing at this time. There's always the option of not testing this for now and leaving testing for the future when we have more of an idea of what we want from this module.

authors: nonprofittechy plocket

repo: SuffolkLITLab/docassemble-AssemblyLine


text:
🛑 Binding Database is down

In d57934d, Binding Database (http://www.bindingdb.org/bind/index.jsp) was down:

HTTP code: 503
Response time: 607 ms

Resolved: Binding Database is back up in 3008f7b.

repo: Sulstice/Uptime-Cheminformatics


text:
🛑 Zinc 15 is down

In 7cb5543, Zinc 15 (https://zinc15.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 15 is back up in 02a7d4b.


text:
🛑 Chem Exper is down

In ddd33bc, Chem Exper (http://www.chemexper.com/) was down:

HTTP code: 0
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Resolved: Chem Exper is back up in f4d4db1.


text:
🛑 Chem Exper is down

In 7f0562e, Chem Exper (http://www.chemexper.com/) was down:

HTTP code: 0
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Resolved: Chem Exper is back up in f163a0d.


text:
🛑 Adverse Drug Reaction Classification System is down

In 978d631, Adverse Drug Reaction Classification System (http://bioinf.xmu.edu.cn/ADReCS/) was down:

HTTP code: 0
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Resolved: Adverse Drug Reaction Classification System is back up in a3109b9.


text:
🛑 Zinc 20 is down

In e6e0264, Zinc 20 (https://zinc20.docking.org) was down:

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Resolved: Zinc 20 is back up in 2b5db59.


text:
🛑 Zinc 15 is down

In 4b67c2d, Zinc 15 (https://zinc15.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 15 is back up in 108aa5d after 16 minutes.


text:
🛑 Chem Exper is down

In 50e948d, Chem Exper (http://www.chemexper.com/) was down:

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text:
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In abd0a10, Chem Exper (http://www.chemexper.com/) was down:

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text:
🛑 Chem Exper is down

In 9a0a63f, Chem Exper (http://www.chemexper.com/) was down:

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Resolved: Chem Exper is back up in 4234699 after 8 minutes.


text:
🛑 Adverse Drug Reaction Classification System is down

In c0a1fa3, Adverse Drug Reaction Classification System (http://bioinf.xmu.edu.cn/ADReCS/) was down:

HTTP code: 0
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Resolved: Adverse Drug Reaction Classification System is back up in 53c5547 after 14 minutes.


text:
🛑 Chem Exper is down

In ae36fac, Chem Exper (http://www.chemexper.com/) was down:

HTTP code: 0
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Resolved: Chem Exper is back up in 0e9e666 after 24 minutes.


text:
🛑 Chem Exper is down

In 50b471f, Chem Exper (http://www.chemexper.com/) was down:

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Resolved: Chem Exper is back up in 900c653 after 7 minutes.


text:
🛑 Chem Exper is down

In 3d8702c, Chem Exper (http://www.chemexper.com/) was down:

HTTP code: 0
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Resolved: Chem Exper is back up in 680104e after 18 minutes.


text:
🛑 Chem Exper is down

In 5f2a742, Chem Exper (http://www.chemexper.com/) was down:

HTTP code: 0
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Resolved: Chem Exper is back up in ddcf95d after 24 minutes.


text:
🛑 Chem Exper is down

In 05fcea3, Chem Exper (http://www.chemexper.com/) was down:

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text:
🛑 Chem Exper is down

In 520595d, Chem Exper (http://www.chemexper.com/) was down:

HTTP code: 0
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Resolved: Chem Exper is back up in 6abd860 after 7 minutes.


text:
🛑 Zinc 20 is down

In 54164b3, Zinc 20 (https://zinc20.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 20 is back up in 4af1ebd after 11 minutes.


text:
🛑 Zinc 20 is down

In c4b65e1, Zinc 20 (https://zinc20.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 20 is back up in d8bb26e after 15 minutes.


text:
🛑 Zinc 15 is down

In 77e24e7, Zinc 15 (https://zinc15.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 15 is back up in 0c27491.


text:
🛑 Zinc 15 is down

In ae6ff2d, Zinc 15 (https://zinc15.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 15 is back up in 5ecb107.


text:
🛑 Zinc 20 is down

In 24307e4, Zinc 20 (https://zinc20.docking.org) was down:

HTTP code: 0
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Resolved: Zinc 20 is back up in b83b827.


text:
🛑 ITK Analysis of Large Histology Datasets is down

In ca8e04e, ITK Analysis of Large Histology Datasets (http://www.na-mic.org/Wiki/index.php/ITK_Analysis_of_Large_Histology_Datasets) was down:

HTTP code: 0
Response time: 0 ms

Resolved: ITK Analysis of Large Histology Datasets is back up in bfdd4eb after 14 minutes.

repo: Sulstice/Uptime-Medical-Informatics


text:
fix fsa assist interrupts fsa jump

I now have a few thousand jumps behind me with this AP and not even one miss align but these 3 lines unfortunately interrupt the fsa during the further jump attempts when the fsa is damaged. If a miss align should take place the fsa must not be interrupted that should not be a problem.

yeah, this is a good change, I've had this happen to me a few times


text:
ED-1229 - Add oci oss support for Build/KnowledgePlatform/SyncTool

updated cloud-store-sdk to 1.4.6 along with associated class file changes
Type of change
Please choose appropriate options.

[ ] Bug fix (non-breaking change which fixes an issue)
[X] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

How Has This Been Tested?
Please describe the tests that you ran to verify your changes in the below checkboxes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration

[X] Executed jenkins build job Build/KnowledgePlatform/SyncTool

@ddevadat - Please handle the conflicts.

@subhashchandrab Most of the changes in this PR available in this PR https://github.com/Sunbird-Knowlg/sunbird-learning-platform/pull/1948. Please validate this and close if this is a duplicate.
cc: @maheshkumargangula

closing this PR


text:
Replace the pie chart reports with dashlet and chart.js

Replace the pie chart reports with dashlet and chart.js across the app

Completed

authors: RNarmatha pandutibil

repo: Sunbird-cQube/community


text:
IP Tunneling with NAT

Hi!
I'd like to connect to an IP tunneling device, that is behind a router. Port forwarding is set up correctly, I can connect with ETS just fine (by ticking the NAT checkbox), but KNXUltimate gives a timeout error almost immediately. I'm assuming, that NAT is not supported at the moment. Would it be possible, to make this work somehow?
Thanks!

Hi
I’m currently on holidays. I’ll take a look next week.

Have a good time, cheers! :)

FYI, I found a JAVA lib, that supports NAT, maybe that could be of some help.
https://github.com/pitschr/knx-core

Thank you!
I had enabled NAT in the past, but many misuses and complaints of users unaware of when to use NAT or when not to use it, had me convinced to remove it.
You are the first one asked me about enabling  NAT. You understand what you're doing..great!
I'm happy to rebuild the HPAI packet with the route back functionality. Please allow me enjoying this vacation's week. I'll dive into it next week.

Hi,
in the meantime, you could try to pass your local IP address, via . localIPAddress, and pass the ehernet interface's name as well, via . KNXEthInterface.
Like this:

// Set the properties
let knxUltimateClientProperties = {
    localIPAddress: "192.168.1.201",
    KNXEthInterface: "eth1"
};

I don't remember if it enables the NAT or not, but next week i'll take a look anyway.

Hi,
could you try, please to unzip this file into the KNXUltimate folder?
src.zip
Then, by setting knxUltimateClientProperties.localSocketAddress={address:'x.x.x.x' , port:yyyy}, you should be able to natting the packets. At least the connect request packet.
Leaving localSocketAddress undefined, it should work as usual.
If you are in trouble, just download the official knxultimate again.

Thanks for the effort, so far I can't get it to work. But please enjoy the rest of your holiday, and don't worry about this... :)

Hmmm... i need to set a test bed, once at home.
Is this your setup?
You ---> | WAN |--->KNX/IP interface

yep, something like that.
Server (running knxUltimate) -> WAN -> KNX/IP interface.
I can give you access to my KNX installation once you get back for testing purposes (in private). But seriously though, disregard my email for the week and do enjoy your vacation! :)

Hi
you know... i'm on the beach doing nothing. So i must do something.
I've tried with the connection from here, via LTE, to my KNX/IP at home.

The telegrams are flowing without issues.
https://github.com/Supergiovane/KNXUltimate/assets/37666636/9a5404a7-64e7-4bca-9b63-9de158cee09b
I'm here on the beach, with my mac using node-red, tethered with my iPhone.
I'm connecting to my home where i've set up a port forwarding to my KNX/IP interface.
Can you enable set the log to DEBUG, and share the results here?

You have definately some firewall filter. I've tried connecting from Italy and Germany, pinging your WAN IP and doing a port scan, but the connection times out every time.

You are right, something must be going on with this, I'll try to figure it out. Thanks for the help! I'll get back to you with the results.

Hi
tried with port 51000 again, without success.
I cannot even ping your WAN IP.

Can you try port 5100 please?

Tried now, but nothing. Timeout. Like with the ping.

Thanks, I'll try figuring it out. I think ping is disabled, but still, KNX should work, ETS connects just fine on port 5100 through my cellular network. But ping is not working for me from my cell as well, so I think it is disabled.
It definitely looks like some sort of geo-blocking, I can telnet to 5100 just fine from my cell and from a hungarian server, but not from DigitalOcean. I'll try to figure it out, and get back to you once it's done.
Thanks again for the help!

OK, I see some strange things here:
This tests to see if I can connect to my KNX installation via UDP.
nc -z -v -u WAN_IP 5100
This succeeds from both the Hungarian and the German server
This tests to see if I can connect to my KNX installation via TCP.
nc -z -v -u WAN_IP 5100
This succeeds only from the Hungarian server.
So one strange thing is that no firewall rules are set in Unifi for these, so both of them should work. Also, KNX should only use UDP, so it shouldn't even matter.

Hi
Please try to connect to my home.
2.44.100.198 Port 51000
Please, if you need,  switch only the group address 4/4/4
Thanks.

Oh my god....
I did not use .Connect(), it was left out in the big copy-and-pasting job.....
So this is where I am now:
It works fine from the Hungarian server (vanilla code, no extra settings). It doesn't work from DigitalOcean. Seems like something happens to TCP packets that come from a different country. Maybe my ISP is doing something, or maybe it's DigitalOcean, I honestly don't know what's happening, but I think I'll just stick to installing it on a Hungarian server.
Thanks so much for bearing with me through this, I can finally get to the real work now :)
And thanks for your work!
Have a nice holiday (at least what's left of it) :)

Thank you! Now it’s shopping time (not mine, my wife).

authors: Lzolcsi Supergiovane

repo: Supergiovane/KNXUltimate


text:
Server Crash

Every so often my private server crashes and Iv come too find out its something to do with your mod (Sorry if Iv done any thing wrong on github, this is my first time using this site) crash-2024-03-16_21.32.30-server.txt
I was told this, when I asked someone for help "alessandrv-enchantments uses a null damage source when they hurt mobs which crashes our mixin but would crash vanilla anyway as damage sources have to be not null" idk if it helps or not.

I believe the issue actually has something to do with this mod.
https://github.com/alessandrv/alessandrv-enchantments
Explosive Enhancement is never mentioned in the log, including the mod list. A "Explosive Enchantment" file is mentioned quite a bit though, which is from the mod linked above. This enchantment seems to be causing the issue.

Oh I must have clicked the wrong github link, Im sorry about that, as I stated before I've never used this before, my bad, Thanks for sending me on the right path.

No worries!


text:
Issue name : Improving the navigation bar UI

Fixed the navigation bar of the UI, issue no. 289.
SSOC member.

try to improve this

The picture isn't visible

The picture isn't visible

okay I will fix it

repo: Susmita-Dey/Sukoon


text:
raycasting

Add simple raycast that can rotate around a single point (points x and y positions also modifiable) that uses the isColliding() function to detect collision with an object

Might create a new script in a new repo and add the feature there

Made new repo, will add it there:
https://github.com/Sv443/JSGame


text:
PFE-14: Added code-tags for payex and changed color for tag secondary…

Description
Updated backgroundcolor for tag class for PayEx
Motivation and Context
Colors for PayEx tags did not exist and the current color scheme needed to be updated.
How Has This Been Tested?
Snapshot on tags component, style lint for less files. Visual review sent to designer.
Screenshots (if appropriate):

Types of changes

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[ ] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[x] I have read the CONTRIBUTING document.
[ ] I have updated the CHANGELOG document.
[ ] I have added tests to cover my changes.
[x] All new and existing tests passed.

Review instructions
Review instructions

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

authors: CLAassistant Fastanton


text:
🛑 lsp.swift-playground.com is down

In 4679908, lsp.swift-playground.com (https://lsp.swift-playground.com/) was down:

HTTP code: 521
Response time: 287 ms

Resolved: lsp.swift-playground.com is back up in 04fe979 after 51 minutes.


text:
Validating for the Mac App Store

Hello,
Great framework you developed here. But I have a problem. When I try to validate an app with this embedded framework I receive an error when uploading/validating:
App Store Connect Operation Error
Bad Bundle Executable. You must include a valid CFBundleExecutable key in the nested bundle libtesseract [com.testapp.pkg/Payload/TestApp.app/Contents/Frameworks/libtesseract.framework] property list file.
Any idea?

Hm, that's a head scratcher. According to the docs:

For a framework, it's the shared library framework and must have the same name as the framework but without the .framework extension.

I pulled down the framework as-is from jcenter and verified that the CFBundleExecutable key was libtesseract. Could you check the value of the Info.plist in the framework embedded in your App bundle and let me know what it says?

Also, could you make a minimal repo that has the same project configuration as you're working with so I can reproduce the issue on my end?

Not sure if this could also potentially be the issue, but there was a person who opened an issue on the main SwiftyTesseract repo having issues related to code signing https://github.com/SwiftyTesseract/SwiftyTesseract/issues/75. Apparently Xcode 12 has an issue where it is not code signing Swift Packages

The problem here I think is that Xcode embeds libtesseract.framework into the app package.
This is a static framework so it shouldn't be embedded. I don't know if it's caused by SwiftPM or Xcode.
Possibly all binary SwiftPM targets are embedded now which will cause issues like this.
Unfortunately, I couldn't yet find a config for changing this behaviour.
I've temporarily solved this by adding a Run Script phase at the end of Build Phases for removing the framework from the package:
rm -rf "$(find $CODESIGNING_FOLDER_PATH -name 'libtesseract.framework')"

This also solves the other issue (https://github.com/SwiftyTesseract/SwiftyTesseract/issues/75) related to missing code signature, since the binary won't be there unsigned.

Hello,
Indeed, the above solution worked, I also discovered it a few hours ago, but I removed the static .framework with Finder :)
I guess this is an XCode issue, it does not matter, but the solution is the one above.
Thanks a million!

authors: Steven0351 ViktorSimko ioanalexme

repo: SwiftyTesseract/libtesseract


text:
DO NOT MERGE: trigger workflow with latest action version

/deploy

You can access the deployment of this PR at https://renku-ci-nb-824.dev.renku.ch

authors: RenkuBot olevski

repo: SwissDataScienceCenter/renku-notebooks


text:
fix: switch from moment to dayjs

closes #4

I have temporarily published @ladjs/simple-recur until this is merged and you do a new release to npm with it.  Thank you!

Please merge, thank you

I'm currently not maintaining this anymore. Maybe @Cliffzz can have a look.
But I think the new Temporal language feature will be the best replacement, once it's in stage 3. See https://github.com/tc39/proposal-temporal.

@niftylettuce Thank you for your PR and sorry for the late reply. I've been pretty busy lately with other things. I'm planning to streamline our open source package maintenance in the near future, github actions ci + automatic dependency updates and releases. So that it's easier to maintain and go over open PRs.
Having said that, I don't think changing the momentjs dependency to dayjs is a good idea. If it ain't broke, don't fix it 😉 Besides that, while I agree there are better options for handling dates than momentjs, momentjs is still being maintained see: https://github.com/moment/moment/releases
I agree with @jamiter, once the temporal language feature is in stage 3 I plan on rewriting this package using that feature in typescript.
So I will close this PR but feel free to maintain your version using dayjs. If you want I can refer dayjs users to your fork in the readme.

It IS broken though.  If you're not aware, there are several core bugs in moment, most of which I've fixed and submitted PR's for... but since moment is no longer maintained... my PR's have gone unmerged.  I wish more people would start listening to me, would make JS a better place.
https://www.theregister.com/2020/08/25/nodejs_dmarc_phishing/
https://www.theregister.com/2020/08/18/nodejs_chromium_patch/

Moment might be broken, but that doesn't mean the code in this package is broken... Can you show me what bugs in moment you are talking about? The only PR that I can find that I assume you are referring to is, https://github.com/moment/moment/pull/5145
I might reconsider if the bugs you are referring to have a big negative impact on this module. Otherwise this is an issue with moment and not this module so please discuss it with moment.

Moment is no longer maintained, the PR's go untouched and uncommented for years.  Please switch to dayjs, it will only make your project better and more lightweight.

authors: Cliffzz jamiter niftylettuce

repo: Swydo/simple-recur


text:
Lock LevelSprite diagonal ratio on resize

When resizing a Sprite using the HandlePoints in the corner, the ratio should be locked (just like Word image resize for example)

Done in commits :
https://github.com/Sygmei/ObEngine/commit/4ccdbe05dfc5ca9715101ab2ca46f4582931a62e
and
https://github.com/Sygmei/ObEngine/commit/afd4ab3a88bb54c6e1befdec3a443bfe5265767e

repo: Sygmei/ObEngine


text:
Rework invoice template on pdf and admin show page

PDF:
Before:

After:

Show Page:
Before:

After:

The "Name" column should be aligned to left, not right :)

Done 👌🏻

Thank you, Tomasz! :tada:

authors: GSadee Tomanhez Zales0123

repo: Sylius/InvoicingPlugin


text:
[WIP] Python code for generating and writing test architecture

This is a very WIP code that in the end should allow to easily define a test FPGA architecture with timings. Currently it generates and writes site types and tile types, no alternative site types supported.

Testarch generated right now can be combined with gen_device_config.yaml in nextpnr_emit to emit testarch database, but when trying to run wire test, nextpnr fails on assertion in line 150 in arch_pack_io.cc

So far this works, testarch capnp is generated and is valid, I'm able to compile it to chip database, and with custom yosys techmaps and flow testarch can pass wire test, but const_wire is still not working.
I'm removing WIP tag, as technically code produces some testarch and correct capnp.

This requires a rebase, and I think we can go ahead and merge once CI is green

authors: acomodi mkurc-ant mtdudek

repo: SymbiFlow/python-fpga-interchange

license: isc


text:
Fix reading GIT tags in Windows OS

Fix reading GIT tags in Windows non-Terminal (ConEmu, git bash, mingw cli, e.g.) CLI interfaces.

Coudl you also extract getTags() method?

Like this?

Thank you

authors: SerafimArts TomasVotruba

repo: Symplify/Symplify


text:
On failed request, provide partial information if available

Presently, the delegate for HTTP error handlers does not accept an HttpRequest class as a parameter. Attempting to provide partial information (i.e. skipping malformed sections of the request, if possible) should be considered.

Was implemented in 0.7.0. If the request line is acceptable, HttpRequest will throw an HttpException on any other request-related errors (which contains request line information). If the request line is not acceptable, HttpRequest throws InvalidDataException to indicate that the request cannot be salvaged.

repo: SynapLink/Zener


text:
use Ubuntu 20.04 from > 2.2 release

As Ubuntu 20.04 LTS has been released it seems a good idea to move to the this new OS.

@paskino, we have multiple problems with the SIRF-exercises. want to give this a go if it happens to work already?

duplicate  #158
Also, forget about doing this before 2.2. But start soon after :wink:

authors: KrisThielemans paskino


text:
数据存储的问题

感谢大佬开源代码，学习ing~学习代码的时候碰到的问题：
1、整个服务并没有链接到数据库
2、上传的数据是保存在内存中吗

这个项目只是个人练手用的，参考一下就好了。

现在已经有数据库了，使用的是Django自带的数据库，当然你也可以改为mysql，这个看网上教程即可
是的，所以一刷新页面数据就没了，想要持久化的话，可以使用 window.localStorage 或 sessionStorage 存储，不过这样的话你还得写一个 能够根据索引标注页面的函数，或者可以试试把标注页面的 innerHTML 给存了，刷新的时候就把之前存储的内容覆盖当前内容就可以了（没试过，个人想法）

repo: Syngou/annotate-system