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<a href="/2019/07/29/Keras模型C-部署最强指南/" class="post-title-link" itemprop="url">Keras模型C++部署最强指南</a>
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<time title="创建时间：2019-07-29 14:33:32 / 修改时间：14:35:58" itemprop="dateCreated datePublished" datetime="2019-07-29T14:33:32+08:00">2019-07-29</time>
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<h3 id="I-Keras保存模型"><a href="#I-Keras保存模型" class="headerlink" title="I.Keras保存模型"></a>I.Keras保存模型</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">model.save(<span class="string">"unet.h5"</span>)</span><br></pre></td></tr></table></figure>
<h3 id="II-Keras模型转换TF模型"><a href="#II-Keras模型转换TF模型" class="headerlink" title="II.Keras模型转换TF模型"></a>II.<a href="https://github.com/amir-abdi/keras_to_tensorflow" target="_blank" rel="noopener">Keras模型转换TF模型</a></h3><figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">python keras_to_tensorflow.py </span><br><span class="line"> --input_model=<span class="string">"path/to/keras/model.h5"</span> </span><br><span class="line"> --output_model=<span class="string">"path/to/save/model.pb"</span></span><br></pre></td></tr></table></figure>
<p>可能遇到的问题是找不到自定义loss，查看相关issues可以找到解决方案。</p>
<h3 id="III-TensorBoard查看模型"><a href="#III-TensorBoard查看模型" class="headerlink" title="III.TensorBoard查看模型"></a>III.<a href="https://www.cnblogs.com/missidiot/p/9509945.html" target="_blank" rel="noopener">TensorBoard查看模型</a></h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf</span><br><span class="line"></span><br><span class="line">model = <span class="string">'model.pb'</span></span><br><span class="line">graph = tf.get_default_graph()</span><br><span class="line">graph_def = graph.as_graph_def()</span><br><span class="line">graph_def.ParseFromString(tf.gfile.FastGFile(model, <span class="string">'rb'</span>).read())</span><br><span class="line">tf.import_graph_def(graph_def, name=<span class="string">'graph'</span>)</span><br><span class="line">summaryWriter = tf.summary.FileWriter(<span class="string">'log/'</span>, graph)</span><br></pre></td></tr></table></figure>
<p>然后使用TB查看输入与输出<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">tensorboard --logdir ./log --host 127.0.0.1 --port 4321</span><br></pre></td></tr></table></figure></p>
<p>可以查看到输入类似这样，这个层的名字C++调用的的时候需要完全匹配<br><img src="/image/1564379915734.png" alt="Alt text"></p>
<h3 id="IV-C-调用TF模型进行前向计算"><a href="#IV-C-调用TF模型进行前向计算" class="headerlink" title="IV.C++调用TF模型进行前向计算"></a>IV.C++调用TF模型进行前向计算</h3><ol>
<li><a href="https://github.com/fo40225/tensorflow-windows-wheel" target="_blank" rel="noopener">点此</a>下载合适的TF预编译DLL（不必与Python训练版本一致），注意不要自行编译，费时又费力坑又多。</li>
<li><p>输入参数转换为Tensor</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line">Tensor Image2Tensor(Image& input)</span><br><span class="line">{</span><br><span class="line"> // 创建tensor</span><br><span class="line"> tensorflow::Tensor image_input = tensorflow::Tensor(tensorflow::DT_FLOAT, </span><br><span class="line"> tensorflow::TensorShape({ 1, input->GetDimensions()[0], input->GetDimensions()[1], 1 }));</span><br><span class="line"> float *tensor_data_ptr = image_input.flat<float>().data();</span><br><span class="line"></span><br><span class="line"> for (int y = 0; y < dims[1]; y++)</span><br><span class="line"> {</span><br><span class="line"> for (int x = 0; x < dims[0]; x++)</span><br><span class="line"> {</span><br><span class="line"> *tensor_data_ptr = input[x][y]/255.0;</span><br><span class="line"> tensor_data_ptr++;</span><br><span class="line"> }</span><br><span class="line"> }</span><br><span class="line"> return image_input;</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
</li>
<li><p>TF载入模型前向计算</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br></pre></td><td class="code"><pre><span class="line">Session* session;</span><br><span class="line">Status status = NewSession(SessionOptions(), &session); <span class="comment">// 创建新会话Session</span></span><br><span class="line"></span><br><span class="line"><span class="built_in">string</span> model_path = <span class="string">"model.pb"</span>; <span class="comment">// 保存的模型路径</span></span><br><span class="line">GraphDef graphdef; <span class="comment">// 当前模型的图定义</span></span><br><span class="line">Status status_load = ReadBinaryProto(Env::Default(), model_path, &graphdef); <span class="comment">// 从pb文件中读取图模型;</span></span><br><span class="line"><span class="keyword">if</span> (!status_load.ok())</span><br><span class="line">{</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << <span class="string">"ERROR: Loading model failed..."</span> << model_path << <span class="built_in">std</span>::<span class="built_in">endl</span>;</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << status_load.ToString() << <span class="string">"\n"</span>;</span><br><span class="line"> <span class="keyword">return</span> <span class="number">-1</span>;</span><br><span class="line">}</span><br><span class="line"></span><br><span class="line">Status status_create = session->Create(graphdef); <span class="comment">// 将图模型导入会话Session中;</span></span><br><span class="line"><span class="keyword">if</span> (!status_create.ok())</span><br><span class="line">{</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << <span class="string">"ERROR: Creating graph in session failed..."</span> << status_create.ToString() << <span class="built_in">std</span>::<span class="built_in">endl</span>;</span><br><span class="line"> <span class="keyword">return</span> <span class="number">-1</span>;</span><br><span class="line">}</span><br><span class="line"><span class="built_in">cout</span> << <span class="string">"Session successfully created."</span> << <span class="built_in">endl</span>;</span><br><span class="line"></span><br><span class="line"><span class="comment">// 开始进行预测</span></span><br><span class="line"><span class="built_in">std</span>::<span class="built_in">vector</span><Tensor> resized_tensors; <span class="comment">// 用于保存读取的图片的tensor数组</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">auto</span> image_tensor = Image2Tensor(image); <span class="comment">// 获取输入Tensor</span></span><br><span class="line"><span class="keyword">const</span> Tensor& resized_tensor = image_tensor;</span><br><span class="line"><span class="built_in">std</span>::<span class="built_in">cout</span> << resized_tensor.DebugString() << <span class="built_in">endl</span>; <span class="comment">// 打印出输入模型的tensor形状</span></span><br><span class="line"><span class="built_in">vector</span><tensorflow::Tensor> outputs;</span><br><span class="line"></span><br><span class="line"><span class="built_in">string</span> output_node = <span class="string">"conv2d_19/Sigmoid"</span>; <span class="comment">// 模型输出层</span></span><br><span class="line"></span><br><span class="line"><span class="comment">// 开始预测，这里的输入名images要和模型的输入相匹配</span></span><br><span class="line">Status status_run = session->Run({ { <span class="string">"input_1"</span>, resized_tensor } }, { output_node }, {}, &outputs);</span><br><span class="line"><span class="keyword">if</span> (!status_run.ok())</span><br><span class="line">{</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << <span class="string">"ERROR: RUN failed..."</span> << <span class="built_in">std</span>::<span class="built_in">endl</span>;</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << status_run.ToString() << <span class="string">"\n"</span>;</span><br><span class="line"> <span class="keyword">return</span> <span class="number">-1</span>;</span><br><span class="line">}</span><br><span class="line"></span><br><span class="line"><span class="comment">// 取出输出值</span></span><br><span class="line"><span class="built_in">std</span>::<span class="built_in">cout</span> << <span class="string">"Output tensor size:"</span> << outputs.size() << <span class="built_in">std</span>::<span class="built_in">endl</span>;</span><br><span class="line"><span class="keyword">for</span> (<span class="built_in">std</span>::<span class="keyword">size_t</span> i = <span class="number">0</span>; i < outputs.size(); i++)</span><br><span class="line">{</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << outputs[i].DebugString() << <span class="built_in">endl</span>; <span class="comment">// 打印出模型输出的tensor的形状</span></span><br><span class="line">}</span><br><span class="line"></span><br><span class="line">Tensor result_tensor = outputs[<span class="number">0</span>]; <span class="comment">// 取出第一个tensor</span></span><br></pre></td></tr></table></figure>
</li>
</ol>
<p>详细例程可以参考<a href="http://www.liuxiao.org/2018/12/tensorflow-c-api-%e4%bb%8e%e8%ae%ad%e7%bb%83%e5%88%b0%e9%83%a8%e7%bd%b2%ef%bc%9a%e4%bd%bf%e7%94%a8-c-api-%e8%bf%9b%e8%a1%8c%e9%a2%84%e6%b5%8b%e5%92%8c%e9%83%a8%e7%bd%b2/" target="_blank" rel="noopener">此链接</a></p>
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<a href="/2019/07/29/C-调用C-编译Dll/" class="post-title-link" itemprop="url">C#调用C++编译Dll</a>
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<h3 id="C-编译DLL"><a href="#C-编译DLL" class="headerlink" title="C++编译DLL"></a>C++编译DLL</h3><p>这里以C++编译数组排序为例<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line">extern "C" __declspec(dllexport) void array_sort(int* a, int len)</span><br><span class="line">{</span><br><span class="line"> for (int i = 0; i < len; i++)</span><br><span class="line"> {</span><br><span class="line"> for (int j = i + 1; j < len; j++)</span><br><span class="line"> {</span><br><span class="line"> if (a[i] > a[j])</span><br><span class="line"> {</span><br><span class="line"> int tmp = a[i];</span><br><span class="line"> a[i] = a[j];</span><br><span class="line"> a[j] = tmp;</span><br><span class="line"> }</span><br><span class="line"> }</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure></p>
<p>注意库的编译方式x86/x64/Debug/Release</p>
<h4 id="C-调用DLL"><a href="#C-调用DLL" class="headerlink" title="C#调用DLL"></a>C#调用DLL</h4><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br></pre></td><td class="code"><pre><span class="line">using System.Runtime.InteropServices;</span><br><span class="line">[DllImport("CppDll.dll", EntryPoint = "array_sort")]</span><br><span class="line"></span><br><span class="line">static extern int array_sort(int[] a, int b);</span><br><span class="line"></span><br><span class="line">private void button1_Click(object sender, EventArgs e)</span><br><span class="line">{</span><br><span class="line"> int[] array = new int[5];</span><br><span class="line"> array[0] = 10;</span><br><span class="line"> array[1] = 12;</span><br><span class="line"> array[2] = 11;</span><br><span class="line"> array[3] = 15;</span><br><span class="line"> array[4] = 13;</span><br><span class="line"> array_sort(array,5);</span><br><span class="line"></span><br><span class="line"> for (int i = 0; i < 5; i++)</span><br><span class="line"> {</span><br><span class="line"> Console.Write(array[i].ToString()+",");</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
<p>同样需要注意库的编译方式x86/x64/Debug/Release，C#中默认为Any CPU</p>
<h4 id="参考："><a href="#参考：" class="headerlink" title="参考："></a>参考：</h4><p>[1].<a href="https://www.codeguru.com/csharp/csharp/cs_data/article.php/c4217/Calling-Unmanaged-Code-Part-1--simple-DLLImport.htm" target="_blank" rel="noopener">https://www.codeguru.com/csharp/csharp/cs_data/article.php/c4217/Calling-Unmanaged-Code-Part-1--simple-DLLImport.htm</a></p>
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<p>vtk内部实现了vtk与mfc/qt的集成控件，但是如果需要跟wpf集成该如何操作呢？<br>实现的方法是由wpf传入一个控件的HWND，作为vtkRenderWindow的父窗口。<br>vtkRenderWindow内部有一个SetParentId方法，可以设置父窗口句柄。<br>此种方法的局限是，vtkRenderWindow的大小无法自适应窗口大小，需要重写原来窗口的resize方法。<br>具体实现示例：<br><figure class="highlight c++"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">void</span> <span class="title">display</span><span class="params">(HWND parent,<span class="keyword">int</span> w,<span class="keyword">int</span> h)</span></span></span><br><span class="line"><span class="function"></span>{</span><br><span class="line"> <span class="comment">//Create a cone</span></span><br><span class="line"> vtkSmartPointer<vtkConeSource> coneSource =</span><br><span class="line"> vtkSmartPointer<vtkConeSource>::New();</span><br><span class="line"> coneSource->Update();</span><br><span class="line"></span><br><span class="line"> <span class="comment">//Create a mapper and actor</span></span><br><span class="line"> vtkSmartPointer<vtkPolyDataMapper> mapper =</span><br><span class="line"> vtkSmartPointer<vtkPolyDataMapper>::New();</span><br><span class="line"> mapper->SetInputConnection(coneSource->GetOutputPort());</span><br><span class="line"></span><br><span class="line"> vtkSmartPointer<vtkActor> actor =</span><br><span class="line"> vtkSmartPointer<vtkActor>::New();</span><br><span class="line"> actor->SetMapper(mapper);</span><br><span class="line"></span><br><span class="line"> <span class="comment">//Create a renderer, render window, and interactor</span></span><br><span class="line"> vtkSmartPointer<vtkRenderer> renderer =</span><br><span class="line"> vtkSmartPointer<vtkRenderer>::New();</span><br><span class="line"> vtkSmartPointer<vtkRenderWindow> renderWindow =</span><br><span class="line"> vtkSmartPointer<vtkRenderWindow>::New();</span><br><span class="line"> renderWindow->AddRenderer(renderer);</span><br><span class="line"> vtkSmartPointer<vtkRenderWindowInteractor> renderWindowInteractor =</span><br><span class="line"> vtkSmartPointer<vtkRenderWindowInteractor>::New();</span><br><span class="line"> renderWindowInteractor->SetRenderWindow(renderWindow);</span><br><span class="line"></span><br><span class="line"> <span class="comment">//Add the actors to the scene</span></span><br><span class="line"> renderer->AddActor(actor);</span><br><span class="line"> renderer->SetBackground(<span class="number">.3</span>, <span class="number">.2</span>, <span class="number">.1</span>); <span class="comment">// Background color dark red</span></span><br><span class="line"> renderWindow->SetSize(w,h);</span><br><span class="line"> renderWindow->SetParentId(parent);</span><br><span class="line"> renderWindow->Render();</span><br><span class="line"> renderWindowInteractor->Start();</span><br><span class="line">}</span><br></pre></td></tr></table></figure></p>
<p>REF:<br>[1].<a href="https://stackoverflow.com/questions/30301087/vtk-render-into-c-sharp/30301203#30301203" target="_blank" rel="noopener">https://stackoverflow.com/questions/30301087/vtk-render-into-c-sharp/30301203#30301203</a></p>
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<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br></pre></td><td class="code"><pre><span class="line">from sklearn.datasets import make_moons</span><br><span class="line">from keras.models import Sequential</span><br><span class="line">from keras.layers import Dense</span><br><span class="line">from keras.callbacks import EarlyStopping</span><br><span class="line">from keras.callbacks import ModelCheckpoint</span><br><span class="line">from matplotlib import pyplot</span><br><span class="line">from keras.models import load_model</span><br><span class="line"># 生成2D分类数据集</span><br><span class="line">X, y = make_moons(n_samples=100, noise=0.2, random_state=1)</span><br><span class="line"># 切割训练/测试集</span><br><span class="line">n_train = 30</span><br><span class="line">trainX, testX = X[:n_train, :], X[n_train:, :]</span><br><span class="line">trainy, testy = y[:n_train], y[n_train:]</span><br><span class="line"># 定义模型</span><br><span class="line">model = Sequential()</span><br><span class="line">model.add(Dense(500, input_dim=2, activation='relu'))</span><br><span class="line">model.add(Dense(1, activation='sigmoid'))</span><br><span class="line">model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])</span><br><span class="line"># 设置early stop</span><br><span class="line">es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200) # patience表示在n个epoch上没有提升时停止</span><br><span class="line">mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)</span><br><span class="line"># 训练模型</span><br><span class="line">history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=4000, verbose=0, callbacks=[es, mc])</span><br><span class="line"># 加载模型</span><br><span class="line">saved_model = load_model('best_model.h5')</span><br><span class="line"># 评估模型性能</span><br><span class="line">_, train_acc = saved_model.evaluate(trainX, trainy, verbose=0)</span><br><span class="line">_, test_acc = saved_model.evaluate(testX, testy, verbose=0)</span><br><span class="line">print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))</span><br></pre></td></tr></table></figure>
<p>[1].<a href="https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/" target="_blank" rel="noopener">https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/</a></p>
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