具体的思路
使用.net下面的TensorFlow框架tensorflowSharp（貌似还是没脱离了框架）.去调用pb文件，然后做成.net core web API 对外提供服务。
具体的实现
直接上代码，非常简单，本身设计到tensorflowsharp的地方非常的少
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]);
}
ImageUtil这个类库是tensorflowSharp官方的例子中一个把图片转成tensor的类库，我直接copy过来了，根据我的网络，修改了几个参数。
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://www.easck.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;
}
}
