ASP.NET MVC - 定制属于你自己的ViewEngine

ASP.NET MVC ViewEngine 原理与自定义实战 ViewEngine 是 ASP.NET MVC 中负责视图定位与渲染的核心抽象机制,其本质是解耦‘找视图’与‘渲染视图’的职责分层设计。它基于 IView 和 IViewEngine 接口契约,通过 ViewEngineResult 封装查找上下文,并依赖 ViewEngineCollection 实现多引擎优先级调度。该机制不仅支撑 .aspx、.ascx 等原生模板,更可扩展支持 StringTemplate、NVelocity 等第三方引擎,显著提升应用的模板灵活性与国际化能力。在政府系统双语切换、 阅读详情

ASP.NET MVC出来这么久了,心中却又很多的疑惑:为什么所有的View都要放在Views目录下? 为什么Shared文件夹下面的页面可以被共享? 为什么Page既可以是*.cshtml,也可以是*.aspx? 

其实上面的几个问题归结起来都是视图引擎的功效。

在传统的ASP.NET中,可能还没有ViewEngine的概念。因为在Web From里面,实现Page实现了IHttpHanlder的接口,所以Page既是响应的处理类,也是视图的渲染类。在ASP.NET MVC中,视图的概念被抽象了出来,试图引擎的概念也被抽象成了一个接口。

首先来看一下IViewEngine接口的定义:

namespace System.Web.Mvc
{
    public interface IViewEngine
    {
        ViewEngineResult FindPartialView(ControllerContext controllerContext, string partialViewName, bool useCache);
        ViewEngineResult FindView(ControllerContext controllerContext, string viewName, string masterName, bool useCache);
        void ReleaseView(ControllerContext controllerContext, IView view);
    }
}

总共3个函数,总结起来大概就是两个功能:Find & Release。


默认情况下,ASP.NET MVC提供了两个视图引擎:WebFormViewEngine和RazorViewEngine。

namespace System.Web.Mvc
{
    public static class ViewEngines
    {
        private static readonly ViewEngineCollection _engines = new ViewEngineCollection
        {
            new WebFormViewEngine(),
            new RazorViewEngine(),
        };

        public static ViewEngineCollection Engines
        {
            get { return _engines; }
        }
    }
}

这就是为什么ASP.NET MVC既支持*.aspx,又支持*.cshtml的原因了(个人觉得如果已经确定要使用RazorView的话,不如把WebFormViewEngine给移除,可能对性能会有所帮助)。

那为什么所有的视图都要放在Views目录下呢,这个就要拜RazorViewngines所赐了。

下面是RazorViewEngine的构造函数:

        public RazorViewEngine(IViewPageActivator viewPageActivator)
            : base(viewPageActivator)
        {
            AreaViewLocationFormats = new[]
            {
                "~/Areas/{2}/Views/{1}/{0}.cshtml",
                "~/Areas/{2}/Views/{1}/{0}.vbhtml",
                "~/Areas/{2}/Views/Shared/{0}.cshtml",
                "~/Areas/{2}/Views/Shared/{0}.vbhtml"
            };
            AreaMasterLocationFormats = new[]
            {
                "~/Areas/{2}/Views/{1}/{0}.cshtml",
                "~/Areas/{2}/Views/{1}/{0}.vbhtml",
                "~/Areas/{2}/Views/Shared/{0}.cshtml",
                "~/Areas/{2}/Views/Shared/{0}.vbhtml"
            };
            AreaPartialViewLocationFormats = new[]
            {
                "~/Areas/{2}/Views/{1}/{0}.cshtml",
                "~/Areas/{2}/Views/{1}/{0}.vbhtml",
                "~/Areas/{2}/Views/Shared/{0}.cshtml",
                "~/Areas/{2}/Views/Shared/{0}.vbhtml"
            };

            ViewLocationFormats = new[]
            {
                "~/Views/{1}/{0}.cshtml",
                "~/Views/{1}/{0}.vbhtml",
                "~/Views/Shared/{0}.cshtml",
                "~/Views/Shared/{0}.vbhtml"
            };
            MasterLocationFormats = new[]
            {
                "~/Views/{1}/{0}.cshtml",
                "~/Views/{1}/{0}.vbhtml",
                "~/Views/Shared/{0}.cshtml",
                "~/Views/Shared/{0}.vbhtml"
            };
            PartialViewLocationFormats = new[]
            {
                "~/Views/{1}/{0}.cshtml",
                "~/Views/{1}/{0}.vbhtml",
                "~/Views/Shared/{0}.cshtml",
                "~/Views/Shared/{0}.vbhtml"
            };

            FileExtensions = new[]
            {
                "cshtml",
                "vbhtml",
            };
        }

所有的寻址路径都被格式化了,是不是很眼熟呢,关于这里为啥用数组而不用List,个人觉得,数组的寻址效率要更高些,遍历速度更快。

好了,找了“罪魁祸首”,就好好地调教一个,让它乖乖听话,小样让去哪就去哪里。

namespace MvcApplicatin.Web.Mvc.ViewEngine
{
    public sealed class CustomViewEngine : RazorViewEngine
    {

        public CustomViewEngine()
        {
            ViewLocationFormats = new[]
            {
                "~/Views/{1}/{0}.cshtml",
                "~/Views/Shared/{0}.cshtml",
                "~/CustomViewLocation/{1}/{0}.cshtml"
            };
        }
        public override ViewEngineResult FindView(ControllerContext controllerContext, string viewName, string masterName, bool useCache)
        {
            return base.FindView(controllerContext, viewName, masterName, useCache);
        }
    }
}

我使用开发语言是C#,视图是Razor,所以稍稍修改了下ViewLocationFormats,让寻址路径能够寻到CustomViewLocation下面的视图。

接下去就很简单了,只需要把原来的视图引擎清空,加载自己的视图引擎就可以了。

        protected void Application_Start()
        {
            AreaRegistration.RegisterAllAreas();

            ViewEngines.Engines.Clear();

            ViewEngines.Engines.Add(new CustomViewEngine());

            RegisterGlobalFilters(GlobalFilters.Filters);
            RegisterRoutes(RouteTable.Routes);
        }




ASP.NET MVC 1.0 ViewEngine 深度解析:渲染中枢设计与实践 ViewEngine 是 ASP.NET MVC 呈现层的核心抽象,本质是连接 Controller 与视图的渲染调度器,而非具体引擎实现。其设计基于接口契约(IViewEngine/IView)、抽象工厂模式与路径级缓存(ViewLocationCache),解决早期 .NET Framework 2.0/3.5 环境下视图查找、编译与输出的性能瓶颈。技术价值在于解耦呈现逻辑与业务逻辑,支撑多格式视图共存、fallback 降级与热更新。典型应用场景包括遗留系统迁移、XML/Excel 等非 HTML 视 阅读详情

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

使用StringTemplate作为ViewEngine的ASP.Net的MVC(一)

我已经爱上了简单的MVC框架,从第一天开始。 但我一直觉得自己的陪同ViewEngine需要一个同样简单的最大价值。在与ASP.Net的MVC预览3以来,我很早就意识到,如果我没有找到一个ViewEngine我能得到的局,我会发现自己跑在输给... WebForms (喘气) 。好消息 : ASP.Net的MVC提供4 ViewEngines开箱!一个坏消息 :我真的不喜欢使用其中的任

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