// Convert the image in filename to a Tensor suitable as input to the Inception model.
static TFTensor CreateTensorFromImageFile(string file)
{
var contents = File.ReadAllBytes(file);
// DecodeJpeg uses a scalar String-valued tensor as input.
var tensor = TFTensor.CreateString(contents);
TFGraph graph;
TFOutput input, output;
// Construct a graph to normalize the image
ConstructGraphToNormalizeImage(out graph, out input, out output);
// 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(byte[] imageTex, TFDataType destinationDataType = TFDataType.Float)
{
var contents = imageTex;
// 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]);
}
}
}
// Convert the image in filename to a Tensor suitable as input to the Inception model.
public static TFTensor CreateTensorFromImageFile(string file, int targetWidth, int targetHeight, 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, targetWidth, targetHeight, 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];
// }
//}
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]);
}
}
}
private static async Task <TFTensor> ReadTensorStringFromFile(string fileName, CancellationToken ct)
{
var content = await File.ReadAllBytesAsync(fileName, ct).ConfigureAwait(false);
return(TFTensor.CreateString(content));
}
public TFTensor 转换方法()
{
//if (b)
//{
// contents = File.ReadAllBytes(file);
// // DecodeJpeg uses a scalar String-valued tensor as input.
// tensor = TFTensor.CreateString(contents);
// b = false;
//}
contents = File.ReadAllBytes(file);
//// DecodeJpeg uses a scalar String-valued tensor as input.
tensor = TFTensor.CreateString(contents);
//contents = null;
// Construct a graph to normalize the image 归一化
// Execute that graph to normalize this one image 执行图规范化这个形象
//using (var session = new TFSession(graph1))
//{
// var normalized = session.Run(
// inputs: new[] { input },
// inputValues: new[] { tensor },
// outputs: new[] { output });
// //tensor = null;
// return normalized[0];
//}
//using (var session = new TFSession(graph1))
//{
//var session = new TFSession(graph1);
// var runner = session.GetRunner();
// runner.AddInput(input, tensor);
// runner.Fetch(output);
// normalized = runner.Run();
// //session.CloseSession();
// session.DeleteSession();
// runner = null;
// //return normalized[0];
////}
//return 1;
using (var session = new TFSession(graph1))
{
try
{
var runner = session.GetRunner();
runner.AddInput(input, tensor);
runner.Fetch(output);
normalized = runner.Run();
return(normalized[0]);
}
catch (Exception e)
{
//Console.WriteLine(e);
throw;
}
finally
{
session.Dispose();
//session.CloseSession();
//session.DeleteSession();
}
}
//normalized = session.Run(
// inputs: new[] { input },
// inputValues: new[] { tensor },
// outputs: new[] { output });
//tensor = null;
//return normalized[0];
}
public void Export(string newModelDir)
{
if (newModelDir.Last() != '/' && newModelDir.Last() != '\\')
{
newModelDir += "/";
}
TFOutput NodeSaver, NodeSaverPath;
//if (!ForTraining)
{
NodeSaver = Graph["save_1/control_dependency"][0];
NodeSaverPath = Graph["save_1/Const"][0];
}
//else
//{
// NodeSaver = Graph["save_2/control_dependency"][0];
// NodeSaverPath = Graph["save_2/Const"][0];
//}
Directory.CreateDirectory(newModelDir);
if (Directory.Exists(newModelDir + "variables"))
{
Directory.Delete(newModelDir + "variables", true);
}
Directory.CreateDirectory(newModelDir + "variables");
foreach (var fileName in Directory.EnumerateFiles(ModelDir))
{
string Source = fileName;
string Destination = newModelDir + Helper.PathToNameWithExtension(fileName);
bool AreSame = false;
try
{
AreSame = Helper.NormalizePath(Source) == Helper.NormalizePath(Destination);
}
catch { }
if (!AreSame)
{
File.Copy(fileName, newModelDir + Helper.PathToNameWithExtension(fileName), true);
}
}
TFTensor TensorPath = TFTensor.CreateString(Encoding.ASCII.GetBytes(newModelDir + "variables/variables"));
var Runner = Session.GetRunner().AddInput(NodeSaverPath, TensorPath);
Runner.Run(NodeSaver);
if (Directory.EnumerateDirectories(newModelDir + "variables", "variables_temp*").Count() > 0)
{
string TempName = Directory.EnumerateDirectories(newModelDir + "variables", "variables_temp*").First();
foreach (var oldPath in Directory.EnumerateFiles(TempName))
{
string OldName = Helper.PathToNameWithExtension(oldPath);
string NewName = "variables" + OldName.Substring(OldName.IndexOf("."));
string NewPath = newModelDir + "variables/" + NewName;
File.Move(oldPath, NewPath);
}
Directory.Delete(TempName, true);
}
TensorPath.Dispose();
}