C# 利用tensorflowsharp调用Tesnorflow模型预测

训练模型的生成
C#中部署.pb训练模型

有时我们会用到C#来部署tensorflow或者keras的训练模型，其中会就涉及到模型的加载和调用。C#已经开发了相关支持包TensorFlowSharp。该插件可以在NuGet中获取并安装。当前使用的VS2015, Python 3.6,Pycharm2018.1 ,tensorflow2.0

训练模型的生成

利用python训练模型，这里不再详细介绍如何训练模型。后续会专门来做一章节来介绍这一块内容。当前应用是用以简单的贯序模型做图片的二分类。由于用keras生成的模型是.h5的文件，需要转换成可以用tensorflow调用的.pb文件。转换的相关代码如下。

from keras import layers, modelsfrom keras.preprocessing.image import ImageDataGeneratorimport numpy as npimport tensorflow.compat.v1 as tf1import tensorflow as tfdef seq_model(X_train, X_val, Y_train, Y_val, img_width, img_height, batch_size):nb_train_samples = len(X_train)nb_validation_samples = len(X_val)model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), input_shape=(img_width, img_height, 3)))model.add(layers.Activation(\'relu\'))model.add(layers.MaxPooling2D(pool_size=(2, 2)))model.add(layers.Conv2D(32, (3, 3)))model.add(layers.Activation(\'relu\'))model.add(layers.MaxPooling2D(pool_size=(2, 2)))model.add(layers.Conv2D(64, (3, 3)))model.add(layers.Activation(\'relu\'))model.add(layers.MaxPooling2D(pool_size=(2, 2)))model.add(layers.Flatten())model.add(layers.Dense(64))model.add(layers.Activation(\'relu\'))model.add(layers.Dropout(0.5))model.add(layers.Dense(1))model.add(layers.Activation(\'sigmoid\'))model.compile(loss=\'binary_crossentropy\',optimizer=\'rmsprop\',metrics=[\'accuracy\'])model.summary()train_datagen = ImageDataGenerator(rescale=1. / 255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)val_datagen = ImageDataGenerator(rescale=1. / 255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)train_generator = train_datagen.flow(np.array(X_train), Y_train, batch_size=batch_size)validation_generator = val_datagen.flow(np.array(X_val), Y_val, batch_size=batch_size)history = model.fit_generator(train_generator,steps_per_epoch=nb_train_samples // batch_size,epochs=100,validation_data=validation_generator,validation_steps=nb_validation_samples // batch_size)model.save_weights(\'model_wieghts.h5\')model.save(\'model_keras.h5\')print(\'input is :\', model.input.name)print(\'output is:\', model.output.name)tf1.reset_default_graph()tf1.keras.backend.set_learning_phase(0)  # 调用模型前一定要执行该命令tf1.disable_v2_behavior()  # 禁止tensorflow2.0的行为network = tf.keras.models.load_model(\'model_keras.h5\')frozen_graph = freeze_session(tf1.keras.backend.get_session(),output_names=[out.op.name for out in network.outputs])tf1.train.write_graph(frozen_graph, \"D:\\AI Vision\\AICore\", \"saved2pb.pb\", as_text=False)

在训练完之后，请一定要记下他的输入层和输出层名称，在C#中调用时需要用作配置参数。

C#中部署.pb训练模型

注意事项
在C#中如果需要调用tesorflow加载训练模型。

.net版本在4.6以上，本版本使用4.7.2

2.使用64位系统，去掉勾选Prefer 32-bit。.引用命名空间

using System;using System.Collections.Generic;using System.Linq;using System.Text;using System.Threading.Tasks;using System.Drawing.Imaging;using System.IO;using TensorFlow;using LT.ST;using System.Drawing;using LT.ST.Element;

读取模型

//modelFile为.pb训练文件路径private TFGraph LoadModel(string modelFile){var g = new TFGraph();var model = File.ReadAllBytes(modelFile);g.Import(model, \"\");return g;}

获取输入图形张量

//inputFileName  输入图片的路径public unsafe TFTensor CreateTensorFromImageFileAlt(string inputFileName, TFDataType destinationDataType = TFDataType.Float){Bitmap bitmap = new Bitmap(inputFileName);//需要将图片处理成训练样本的相同大小int w = dataDirectorLocal.CurrProject.fWidth;//训练时图片宽度int h = dataDirectorLocal.CurrProject.fHeighth;//训练时图片高度Bitmap resizedBmp = new Bitmap(w, h);Graphics g = Graphics.FromImage(resizedBmp);//设置高质量插值法g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.High;//设置高质量,低速度呈现平滑程度g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality;g.CompositingQuality = System.Drawing.Drawing2D.CompositingQuality.HighQuality;//消除锯齿g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.AntiAlias;g.DrawImage(bitmap, new Rectangle(0, 0, w, h), new Rectangle(0, 0, bitmap.Width, bitmap.Height), GraphicsUnit.Pixel);BitmapData data = resizedBmp.LockBits(new Rectangle(0, 0, resizedBmp.Width, resizedBmp.Height), ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb);var matrix = new float[1, resizedBmp.Width, resizedBmp.Height, 3];byte* scan0 = (byte*)data.Scan0.ToPointer();for (int i = 0; i < data.Height; i++){for (int j = 0; j < data.Width; j++){byte* pixelData = scan0 + i * data.Stride + j * 3;matrix[0, i, j, 0] = pixelData[2];matrix[0, i, j, 1] = pixelData[1];matrix[0, i, j, 2] = pixelData[0];}}resizedBmp.UnlockBits(data);TFTensor tensor = matrix;g.Dispose();resizedBmp.Dispose();bitmap.Dispose();return tensor;}

开始测试和预测

public bool TensorAnalysisFromFile(string modelFile, string[] labelsFiles){// 创建图try{var g = new TFGraph();g = LoadModel(modelFile);dataDirectorLocal.Images.Clear();foreach (var labelsFile in labelsFiles){// 定义常量using (var session = new TFSession(g)){var tensor = CreateTensorFromImageFileAlt(labelsFile);var runner = session.GetRunner();//训练时的输入和输出名称runner.AddInput(g[\"conv2d_1_input\"][0], tensor);runner.Fetch(g[\"activation_5/Sigmoid\"][0]);var output = runner.Run();var result = output[0];var rshape = result.Shape;if (result.NumDims != 2 || rshape[0] != 1){var shape = \"\";foreach (var d in rshape){shape += $\"{d} \";}shape = shape.Trim();Console.WriteLine($\"Error: expected to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape [{shape}]\");Environment.Exit(1);}bool jagged = true;var bestIdx = 0;float p = 0, best = 0;var probabilities = ((float[][])result.GetValue(jagged: true))[0];ImageStruct img = new ImageStruct();img.strSource = labelsFile;img.Factor = (float)probabilities[0];img.Name = img.Factor < 0.5 ? dataDirectorLocal.LabelCatetorys[1].Name : dataDirectorLocal.LabelCatetorys[2].Name;img.backgroud2 = img.Factor < 0.5 ? dataDirectorLocal.LabelCatetorys[1].Color : dataDirectorLocal.LabelCatetorys[2].Color;img.iTested = img.Factor < 0.5 ? dataDirectorLocal.LabelCatetorys[1].Key : dataDirectorLocal.LabelCatetorys[2].Key;dataDirectorLocal.Images.Add(img);}}}catch (Exception e){return false;}return true;}

程序测试成功如下