2017-02-25:发布OrangeUI 1.66

下载地址(百度网盘):https://pan.baidu.com/s/14i84u

附带有控件安装教程,使用说明,及示例.


1. 内置TSystemHttpControl(uBaseHttpControl.pas)来下载图片,
Image下载URL图片不需要再添加uIdHttpControl.pas或uNativeHttpControl,
也不需要手动再指定DownloadPictureHttpControlClass
2. 升级DownloadPictureManager,
DrawPicture(如Image.Properties.Picture)和ListBox的图标(如ListBoxItem.Icon) 默认使用全局的GlobalDownloadPictureManager来下载图片的
可以为DrawPicture指定DownloadPictureManager,
如果DrawPicture之前设置过URL,再设置新的URL之后,要等到新URL下载完成之后图片才会切换显示过来,
可以为ListBox指定DownloadPictureManager,这样可以把ListBox的图标单独放在一个文件夹里面,
可以设置DownloadPictureManager在下载图片时的四种状态图片(等待下载,下载中,下载失败,图片出错),
具体请查看OrangeUIDemo中的DownloadPictureManager示例和OrangeViewNews示例。
3. ListBox.Prop.Items增加搜索Item的方法FindIteByDetail,FindItemByDetail*
4. 添加一个简单的图片上传下载客户端和服务端的简单示例
5. 添加一个微信朋友圈客户端和服务端的简单示例
以下转载自官方软件介绍 通过OrangeUI,您可以快速及稳定的实现如下功能,并且全部免费: 1.APP主页九宫格菜单,在主流APP中经常能够见到,OrangeUI只需要一个控件,而不是Image和Label堆出来实现。 2.广告图片轮播功能,并且是可以跟随手指滑动切换,这是目前别的控件还做不到的。 3.列表ListView支持直接设置图片的URL,通过底层的多线程下载功能,可以轻松实现异步加载图片,并且不会感觉到卡顿。 4.列表框ListView自带下拉刷新、下拉加载的功能,在手机上加载2w条数据只需2秒。 5.APP上数据呈现以ListView为主,列表框ListView支持的设计面板模式,可以在设计面板上添加任意数目的控件,排列好布局,各种样式轻松搞定。 6.实现稳定的页面切换效果,让您的APP如原生般的用户体验(APP最注重的就是用户体验)。 7.各种通用的界面,如等待框,对话框,菜单框,拍照菜单框,选择框等。 8.可以快速生成IOS和Android平台下所用到的各种尺寸的程序图标和启动界面图片。 9.网上商城、好友聊天、新闻浏览、外贸验货、平板点单等示例,包含全部源码的。 10.可以手势切换的分页控件,加入到您的APP中可以极大的方便用户进行操作。 11.稳定灵活的Frame开发方案(发布会李维老师推荐),可以很好的将复杂的主窗体分解成四、五个小页面,加快页面的截入速度,减少内存占用,并且按返回键自动返回上一页的处理,让你打造出高效的APP。 12.开源的微信接口、微博接口、阿里接口、支付宝支付、微信支付、推送功能源码,让你的APP更强大。 13.简单实用的图片HTTP上传下载客户端和服务端(IndyHttpServer)的示例源码。 14.发朋友圈、查看朋友圈的客户端和后台服务端(DataSnap)的示例源码。 15.按钮在ScrollBox上用手指滑动不会触发点击事件。 16.编辑框在ScrollBox上用手指滑动时不会触发输入事件,并已自动处理虚拟键盘显示/隐藏事件,不会挡住编辑框。 17.列表ListView支持在设计时添加Item并能即时预览到效果,目前自带和别的控件都做不到的。 18.OrangeUI的用户目前已经超过200名,用户开发的APP也不下百个,不少都上架到AppStore,腾讯应用宝等市场。 19.每个控件配备专门的DEMO和文档教程,使用起来更轻松。 20.提供专门的OrangeUI技术支持QQ群(群号:10900297),也可以加我QQ452330643,提供专业的APP开发支持。 21.定期一至两个月更新一次,不断添加新的控件适应新的趋势,以及新的实用示例。
(base) root@74fb9740dd84:/workspace/data/CH4/01.train# dp train input.json 2025-11-25 05:58:04.052073: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-11-25 05:58:04.057367: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1764050284.064012 710 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1764050284.066338 710 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1764050284.071566 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1764050284.071581 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1764050284.071583 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1764050284.071584 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. 2025-11-25 05:58:04.073369: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX_VNNI AVX_VNNI_INT8 AVX_NE_CONVERT, in other operations, rebuild TensorFlow with the appropriate compiler flags. To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and DP_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. DeePMD-kit: Successfully load libcudart.so.12 [2025-11-25 05:58:08,701] DEEPMD INFO Calculate neighbor statistics... (add --skip-neighbor-stat to skip this step) [2025-11-25 05:58:08,737] DEEPMD INFO If you encounter the error 'an illegal memory access was encountered', this may be due to a TensorFlow issue. To avoid this, set the environment variable DP_INFER_BATCH_SIZE to a smaller value than the last adjusted batch size. The environment variable DP_INFER_BATCH_SIZE controls the inference batch size (nframes * natoms). [2025-11-25 05:58:09,883] DEEPMD INFO Neighbor statistics: training data with minimal neighbor distance: 1.042950 [2025-11-25 05:58:09,883] DEEPMD INFO Neighbor statistics: training data with maximum neighbor size: [4 1] (cutoff radius: 6.000000) [2025-11-25 05:58:09,902] DEEPMD INFO _____ _____ __ __ _____ _ _ _ [2025-11-25 05:58:09,902] DEEPMD INFO | __ \ | __ \ | \/ || __ \ | | (_)| | [2025-11-25 05:58:09,902] DEEPMD INFO | | | | ___ ___ | |__) || \ / || | | | ______ | | __ _ | |_ [2025-11-25 05:58:09,902] DEEPMD INFO | | | | / _ \ / _ \| ___/ | |\/| || | | ||______|| |/ /| || __| [2025-11-25 05:58:09,902] DEEPMD INFO | |__| || __/| __/| | | | | || |__| | | < | || |_ [2025-11-25 05:58:09,902] DEEPMD INFO |_____/ \___| \___||_| |_| |_||_____/ |_|\_\|_| \__| [2025-11-25 05:58:09,902] DEEPMD INFO Please read and cite: [2025-11-25 05:58:09,902] DEEPMD INFO Wang, Zhang, Han and E, Comput.Phys.Comm. 228, 178-184 (2018) [2025-11-25 05:58:09,902] DEEPMD INFO Zeng et al, J. Chem. Phys., 159, 054801 (2023) [2025-11-25 05:58:09,902] DEEPMD INFO Zeng et al, J. Chem. Theory Comput., 21, 4375-4385 (2025) [2025-11-25 05:58:09,902] DEEPMD INFO See https://deepmd.rtfd.io/credits/ for details. [2025-11-25 05:58:09,902] DEEPMD INFO --------------------------------------------------------------------------------------- [2025-11-25 05:58:09,902] DEEPMD INFO installed to: /opt/deepmd-kit/lib/python3.12/site-packages/deepmd [2025-11-25 05:58:09,902] DEEPMD INFO source: [2025-11-25 05:58:09,902] DEEPMD INFO source branch: HEAD [2025-11-25 05:58:09,902] DEEPMD INFO source commit: eeadafb [2025-11-25 05:58:09,902] DEEPMD INFO source commit at: 2025-11-05 14:55:36 +0100 [2025-11-25 05:58:09,902] DEEPMD INFO use float prec: double [2025-11-25 05:58:09,902] DEEPMD INFO build variant: cuda [2025-11-25 05:58:09,902] DEEPMD INFO Backend: TensorFlow [2025-11-25 05:58:09,902] DEEPMD INFO TF ver: unknown [2025-11-25 05:58:09,902] DEEPMD INFO build with TF ver: 2.19.1 [2025-11-25 05:58:09,902] DEEPMD INFO build with TF inc: /opt/deepmd-kit/lib/python3.12/site-packages/tensorflow/include/ [2025-11-25 05:58:09,902] DEEPMD INFO /opt/deepmd-kit/include [2025-11-25 05:58:09,902] DEEPMD INFO build with TF lib: [2025-11-25 05:58:09,902] DEEPMD INFO running on: 74fb9740dd84 [2025-11-25 05:58:09,902] DEEPMD INFO computing device: gpu:0 [2025-11-25 05:58:09,902] DEEPMD INFO CUDA_VISIBLE_DEVICES: unset [2025-11-25 05:58:09,902] DEEPMD INFO Count of visible GPUs: 1 [2025-11-25 05:58:09,902] DEEPMD INFO num_intra_threads: 0 [2025-11-25 05:58:09,902] DEEPMD INFO num_inter_threads: 0 [2025-11-25 05:58:09,902] DEEPMD INFO --------------------------------------------------------------------------------------- [2025-11-25 05:58:09,930] DEEPMD INFO ---Summary of DataSystem: training ----------------------------------------------- [2025-11-25 05:58:09,930] DEEPMD INFO found 1 system(s): [2025-11-25 05:58:09,930] DEEPMD INFO system natoms bch_sz n_bch prob pbc [2025-11-25 05:58:09,930] DEEPMD INFO ../00.data/training_data 5 7 22 1.000e+00 T [2025-11-25 05:58:09,930] DEEPMD INFO -------------------------------------------------------------------------------------- [2025-11-25 05:58:09,953] DEEPMD INFO ---Summary of DataSystem: validation ----------------------------------------------- [2025-11-25 05:58:09,953] DEEPMD INFO found 1 system(s): [2025-11-25 05:58:09,953] DEEPMD INFO system natoms bch_sz n_bch prob pbc [2025-11-25 05:58:09,953] DEEPMD INFO ../00.data/validation_data 5 7 5 1.000e+00 T [2025-11-25 05:58:09,953] DEEPMD INFO -------------------------------------------------------------------------------------- [2025-11-25 05:58:09,953] DEEPMD INFO training without frame parameter [2025-11-25 05:58:09,953] DEEPMD INFO data stating... (this step may take long time) [2025-11-25 05:58:10,081] DEEPMD INFO built lr [2025-11-25 05:58:10,427] DEEPMD INFO built network [2025-11-25 05:58:10,890] DEEPMD INFO built training [2025-11-25 05:58:10,891] DEEPMD WARNING To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and DP_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. [2025-11-25 05:58:10,911] DEEPMD INFO initialize model from scratch [2025-11-25 05:58:11,269] DEEPMD INFO start training at lr 1.00e-03 (== 1.00e-03), decay_step 5000, decay_rate 0.950006, final lr will be 3.51e-08 [2025-11-25 05:58:11,683] DEEPMD INFO batch 0: trn: rmse = 1.13e+01, rmse_e = 7.04e-01, rmse_f = 3.56e-01, lr = 1.00e-03 [2025-11-25 05:58:11,683] DEEPMD INFO batch 0: val: rmse = 1.42e+01, rmse_e = 7.06e-01, rmse_f = 4.50e-01 [2025-11-25 05:58:37,056] DEEPMD INFO batch 1000: trn: rmse = 4.89e+00, rmse_e = 3.31e-01, rmse_f = 1.55e-01, lr = 1.00e-03 [2025-11-25 05:58:37,056] DEEPMD INFO batch 1000: val: rmse = 4.17e+00, rmse_e = 3.32e-01, rmse_f = 1.32e-01 [2025-11-25 05:58:37,057] DEEPMD INFO batch 1000: total wall time = 25.79 s [2025-11-25 05:59:01,438] DEEPMD INFO batch 2000: trn: rmse = 4.16e+00, rmse_e = 1.98e-02, rmse_f = 1.31e-01, lr = 1.00e-03 [2025-11-25 05:59:01,438] DEEPMD INFO batch 2000: val: rmse = 3.85e+00, rmse_e = 2.04e-02, rmse_f = 1.22e-01 [2025-11-25 05:59:01,438] DEEPMD INFO batch 2000: total wall time = 24.38 s [2025-11-25 05:59:22,714] DEEPMD INFO batch 3000: trn: rmse = 4.73e+00, rmse_e = 7.67e-02, rmse_f = 1.50e-01, lr = 1.00e-03 [2025-11-25 05:59:22,714] DEEPMD INFO batch 3000: val: rmse = 3.76e+00, rmse_e = 7.63e-02, rmse_f = 1.19e-01 [2025-11-25 05:59:22,714] DEEPMD INFO batch 3000: total wall time = 21.28 s [2025-11-25 05:59:46,195] DEEPMD INFO batch 4000: trn: rmse = 5.26e+00, rmse_e = 2.37e-02, rmse_f = 1.66e-01, lr = 1.00e-03 [2025-11-25 05:59:46,195] DEEPMD INFO batch 4000: val: rmse = 3.79e+00, rmse_e = 2.42e-02, rmse_f = 1.20e-01 [2025-11-25 05:59:46,195] DEEPMD INFO batch 4000: total wall time = 23.48 s [2025-11-25 06:00:09,875] DEEPMD INFO batch 5000: trn: rmse = 4.22e+00, rmse_e = 4.11e-02, rmse_f = 1.37e-01, lr = 9.50e-04 [2025-11-25 06:00:09,876] DEEPMD INFO batch 5000: val: rmse = 4.09e+00, rmse_e = 4.09e-02, rmse_f = 1.33e-01 [2025-11-25 06:00:09,876] DEEPMD INFO batch 5000: total wall time = 23.68 s [2025-11-25 06:00:33,788] DEEPMD INFO batch 6000: trn: rmse = 3.64e+00, rmse_e = 2.27e-02, rmse_f = 1.18e-01, lr = 9.50e-04 [2025-11-25 06:00:33,788] DEEPMD INFO batch 6000: val: rmse = 3.24e+00, rmse_e = 2.27e-02, rmse_f = 1.05e-01 [2025-11-25 06:00:33,789] DEEPMD INFO batch 6000: total wall time = 23.91 s [2025-11-25 06:00:55,546] DEEPMD INFO batch 7000: trn: rmse = 1.47e+01, rmse_e = 6.75e+00, rmse_f = 4.60e-01, lr = 9.50e-04 [2025-11-25 06:00:55,547] DEEPMD INFO batch 7000: val: rmse = 1.39e+01, rmse_e = 6.75e+00, rmse_f = 4.33e-01 [2025-11-25 06:00:55,547] DEEPMD INFO batch 7000: total wall time = 21.76 s [2025-11-25 06:01:19,881] DEEPMD INFO batch 8000: trn: rmse = 1.53e+01, rmse_e = 6.75e+00, rmse_f = 4.80e-01, lr = 9.50e-04 [2025-11-25 06:01:19,881] DEEPMD INFO batch 8000: val: rmse = 1.51e+01, rmse_e = 6.75e+00, rmse_f = 4.73e-01 [2025-11-25 06:01:19,881] DEEPMD INFO batch 8000: total wall time = 24.33 s [2025-11-25 06:01:44,351] DEEPMD INFO batch 9000: trn: rmse = 1.26e+01, rmse_e = 6.74e+00, rmse_f = 3.87e-01, lr = 9.50e-04 [2025-11-25 06:01:44,351] DEEPMD INFO batch 9000: val: rmse = 1.54e+01, rmse_e = 6.75e+00, rmse_f = 4.82e-01 [2025-11-25 06:01:44,351] DEEPMD INFO batch 9000: total wall time = 24.47 s [2025-11-25 06:02:06,170] DEEPMD INFO batch 10000: trn: rmse = 1.55e+01, rmse_e = 6.75e+00, rmse_f = 4.87e-01, lr = 9.03e-04 [2025-11-25 06:02:06,171] DEEPMD INFO batch 10000: val: rmse = 1.35e+01, rmse_e = 6.75e+00, rmse_f = 4.14e-01 [2025-11-25 06:02:06,171] DEEPMD INFO batch 10000: total wall time = 21.82 s [2025-11-25 06:02:06,286] DEEPMD INFO saved checkpoint model.ckpt [2025-11-25 06:02:30,824] DEEPMD INFO batch 11000: trn: rmse = 1.18e+01, rmse_e = 6.74e+00, rmse_f = 3.55e-01, lr = 9.03e-04 [2025-11-25 06:02:30,825] DEEPMD INFO batch 11000: val: rmse = 1.37e+01, rmse_e = 6.75e+00, rmse_f = 4.25e-01 [2025-11-25 06:02:30,825] DEEPMD INFO batch 11000: total wall time = 24.65 s [2025-11-25 06:02:55,313] DEEPMD INFO batch 12000: trn: rmse = 1.50e+01, rmse_e = 6.75e+00, rmse_f = 4.70e-01, lr = 9.03e-04 [2025-11-25 06:02:55,313] DEEPMD INFO batch 12000: val: rmse = 1.43e+01, rmse_e = 6.75e+00, rmse_f = 4.45e-01 [2025-11-25 06:02:55,313] DEEPMD INFO batch 12000: total wall time = 24.49 s [2025-11-25 06:03:19,394] DEEPMD INFO batch 13000: trn: rmse = 1.41e+01, rmse_e = 6.75e+00, rmse_f = 4.38e-01, lr = 9.03e-04 [2025-11-25 06:03:19,394] DEEPMD INFO batch 13000: val: rmse = 1.52e+01, rmse_e = 6.75e+00, rmse_f = 4.77e-01 [2025-11-25 06:03:19,394] DEEPMD INFO batch 13000: total wall time = 24.08 s [2025-11-25 06:03:41,154] DEEPMD INFO batch 14000: trn: rmse = 1.45e+01, rmse_e = 6.75e+00, rmse_f = 4.51e-01, lr = 9.03e-04 [2025-11-25 06:03:41,154] DEEPMD INFO batch 14000: val: rmse = 1.54e+01, rmse_e = 6.75e+00, rmse_f = 4.83e-01 [2025-11-25 06:03:41,155] DEEPMD INFO batch 14000: total wall time = 21.76 s
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