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var graph = new TFGraph();

//重点是下面的这句，把训练好的pb文件给读出来字节，然后导入

var model = File.ReadAllBytes(model_file);

graph.Import(model);

Console.WriteLine("请输入一个图片的地址");

var src = Console.ReadLine();

var tensor = ImageUtil.CreateTensorFromImageFile(src);

using (var sess = new TFSession(graph))

{

var runner = sess.GetRunner();

runner.AddInput(graph["Cast_1"][0], tensor);

var r = runner.Run(graph.softmax(graph["softmax_linear/softmax_linear"][0]));

var v = (float[,])r.GetValue();

Console.WriteLine(v[0,0]);

Console.WriteLine(v[0, 1]);

}

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public static class ImageUtil

{

public static TFTensor CreateTensorFromImageFile(byte[] contents, TFDataType destinationDataType = TFDataType.Float)

{

var tensor = TFTensor.CreateString(contents);

TFOutput input, output;

// Construct a graph to normalize the image

using (var graph = ConstructGraphToNormalizeImage(out input, out output, destinationDataType))

{

// Execute that graph to normalize this one image

using (var session = new TFSession(graph))

{

var normalized = session.Run(

inputs: new[] { input },

inputValues: new[] { tensor },

outputs: new[] { output });

return normalized[0];

}

}

}

// Convert the image in filename to a Tensor suitable as input to the Inception model.

public static TFTensor CreateTensorFromImageFile(string file, TFDataType destinationDataType = TFDataType.Float)

{

var contents = File.ReadAllBytes(file);

// DecodeJpeg uses a scalar String-valued tensor as input.

var tensor = TFTensor.CreateString(contents);

TFOutput input, output;

// Construct a graph to normalize the image

using (var graph = ConstructGraphToNormalizeImage(out input, out output, destinationDataType))

{

// Execute that graph to normalize this one image

using (var session = new TFSession(graph))

{

var normalized = session.Run(

inputs: new[] { input },

inputValues: new[] { tensor },

outputs: new[] { output });

return normalized[0];

}

}

}

// The inception model takes as input the image described by a Tensor in a very

// specific normalized format (a particular image size, shape of the input tensor,

// normalized pixel values etc.).

//

// This function constructs a graph of TensorFlow operations which takes as

// input a JPEG-encoded string and returns a tensor suitable as input to the

// inception model.

private static TFGraph ConstructGraphToNormalizeImage(out TFOutput input, out TFOutput output, TFDataType destinationDataType = TFDataType.Float)

{

// Some constants specific to the pre-trained model at:

// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip

//

// - The model was trained after with images scaled to 224x224 pixels.

// - The colors, represented as R, G, B in 1-byte each were converted to

// float using (value - Mean)/Scale.

const int W = 128;

const int H = 128;

const float Mean = 0;

const float Scale = 1f;

var graph = new TFGraph();

input = graph.Placeholder(TFDataType.String);

output = graph.Cast(

graph.Div(x: graph.Sub(x: graph.ResizeBilinear(images: graph.ExpandDims(input: graph.Cast(graph.DecodeJpeg(contents: input, channels: 3), DstT: TFDataType.Float),

dim: graph.Const(0, "make_batch")),

size: graph.Const(new int[] { W, H }, "size")),

y: graph.Const(Mean, "mean")),

y: graph.Const(Scale, "scale")), destinationDataType);

return graph;

}

}