private static async Task Run()
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_inceptionGraph = new Emgu.TF.Models.Inception(null, so);
_inceptionGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
//_inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
//use a retrained model to recognize followers
await _inceptionGraph.Init(
new string[] { "optimized_graph.pb", "output_labels.txt" },
"https://github.com/emgucv/models/raw/master/inception_flower_retrain/",
"Placeholder",
"final_result");
Stopwatch watch = Stopwatch.StartNew();
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(_inputFileInfo.FullName, 299, 299, 0.0f, 1.0f / 255.0f, false, false);
var results = _inceptionGraph.Recognize(imageTensor);
watch.Stop();
String resStr = String.Format("Object is {0} with {1}% probability. Recognition completed in {2} milliseconds.", results[0].Label, results[0].Probability * 100, watch.ElapsedMilliseconds);
System.Console.WriteLine(resStr);
}
public MainForm()
{
InitializeComponent();
TfInvoke.Init();
messageLabel.Text = String.Empty;
cameraButton.Text = _startCameraText;
//DisableUI();
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_inceptionGraph = new MaskRcnnInceptionV2Coco(null, so);
_inceptionGraph.OnDownloadProgressChanged += OnDownloadProgressChangedEventHandler;
//_inceptionGraph.Init();
}
public CSession(Graph graph, Status s, bool user_XLA = false)
{
var opts = new SessionOptions();
opts.SetConfig(new ConfigProto {
InterOpParallelismThreads = 4
});
session_ = new Session(graph, opts, s);
}
public MultiboxDetectionPage()
: base()
{
Title = "Multibox People Detection";
if (_multiboxGraph == null)
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_multiboxGraph = new MultiboxGraph(null, so);
_multiboxGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
_multiboxGraph.OnDownloadCompleted += onDownloadCompleted;
_multiboxGraph.OnDownloadCompleted += (sender, e) =>
{
OnButtonClicked(sender, e);
};
}
OnImagesLoaded += (sender, image) =>
{
try
{
SetMessage("Please wait...");
SetImage();
Stopwatch watch = Stopwatch.StartNew();
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(image[0], 224, 224, 128.0f, 1.0f / 128.0f);
MultiboxGraph.Result[] detectResult = _multiboxGraph.Detect(imageTensor);
watch.Stop();
Emgu.Models.Annotation[] annotations = MultiboxGraph.FilterResults(detectResult, 0.1f);
var jpeg = Emgu.Models.NativeImageIO.ImageFileToJpeg(image[0], annotations);
watch.Stop();
SetImage(jpeg.Raw, jpeg.Width, jpeg.Height);
#if __MACOS__
var displayImage = this.DisplayImage;
displayImage.WidthRequest = jpeg.Width;
displayImage.HeightRequest = jpeg.Height;
#endif
SetMessage(String.Format("Detected in {0} milliseconds.", watch.ElapsedMilliseconds));
}
catch (Exception excpt)
{
String msg = excpt.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
}
};
}
private static async Task Run()
{
SessionOptions so = new SessionOptions();
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
#if DEBUG
config.LogDevicePlacement = true;
#endif
if (TfInvoke.IsGoogleCudaEnabled)
{
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
}
so.SetConfig(config.ToProtobuf());
_inceptionGraph = new Emgu.TF.Models.Inception(null, so);
_inceptionGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
//_inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
System.Console.WriteLine("Initializing model");
//use a retrained model to recognize followers
await _inceptionGraph.Init(
new string[] { "optimized_graph.pb", "output_labels.txt" },
"https://github.com/emgucv/models/raw/master/inception_flower_retrain/",
"Placeholder",
"final_result");
System.Console.WriteLine("Model initialized.");
Session.Device[] devices = GetSessionDevices(_inceptionGraph.Session);
StringBuilder sb = new StringBuilder();
foreach (Session.Device d in devices)
{
sb.Append(String.Format("{1}: {0}{2}", d.Name, d.Type, Environment.NewLine));
}
System.Console.WriteLine(String.Format("Default Session Devices:{0}{1}", Environment.NewLine, sb.ToString()));
Stopwatch watch = Stopwatch.StartNew();
System.Console.WriteLine("Reading image into tensor");
Tensor imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(_inputFileInfo.FullName, 299, 299, 0.0f, 1.0f / 255.0f, false, false);
System.Console.WriteLine("Running inference...");
var results = _inceptionGraph.Recognize(imageTensor);
watch.Stop();
String resStr = String.Format("Object is {0} with {1}% probability. Recognition completed in {2} milliseconds.", results[0][0].Label, results[0][0].Probability * 100, watch.ElapsedMilliseconds);
System.Console.WriteLine(resStr);
}
public StylizePage()
: base()
{
Title = "Stylize";
if (_stylizeGraph == null)
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_stylizeGraph = new StylizeGraph(null, so);
_stylizeGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
_stylizeGraph.OnDownloadCompleted += onDownloadCompleted;
_stylizeGraph.OnDownloadCompleted += (sender, e) =>
{
OnButtonClicked(sender, e);
};
}
OnImagesLoaded += (sender, image) =>
{
try
{
SetMessage("Please wait...");
SetImage();
Stopwatch watch = Stopwatch.StartNew();
byte[] jpeg = _stylizeGraph.StylizeToJpeg(image[0], 1);
watch.Stop();
SetImage(jpeg);
#if __MACOS__
NSImage img = new NSImage(image[0]);
var displayImage = this.GetImage();
displayImage.WidthRequest = img.Size.Width;
displayImage.HeightRequest = img.Size.Height;
#endif
SetMessage(String.Format("Stylized in {0} milliseconds.", watch.ElapsedMilliseconds));
}
catch (Exception excpt)
{
String msg = excpt.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
}
};
}
public void TestCUDAEnabled()
{
bool cuda = TfInvoke.IsGoogleCudaEnabled;
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.LogDevicePlacement = true;
byte[] pbuff;
using (MemoryStream ms = new MemoryStream())
{
config.WriteTo(ms);
pbuff = ms.ToArray();
}
SessionOptions options = new SessionOptions();
options.SetConfig(pbuff);
Add(3, 4, options);
}
public bool Run()
{
PrepareData();
var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
var options = new SessionOptions();
options.SetConfig(new ConfigProto {
AllowSoftPlacement = true
});
using (var sess = tf.Session(graph, opts: options))
{
Train(sess);
}
return(true);
}
public void TestChooseDevice()
{
SessionOptions so = new SessionOptions();
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
//config.DeviceCount.Add("GPU", 1);
//config.DeviceCount.Add("CPU", 1);
config.GpuOptions = new GPUOptions();
config.GpuOptions.VisibleDeviceList = "0";
//config.GpuOptions.VisibleDeviceList = "0, 1";
//var devicesList = config.GpuOptions.VisibleDeviceList;
//config.LogDevicePlacement = true;
if (TfInvoke.IsGoogleCudaEnabled)
{
so.SetConfig(config.ToProtobuf());
}
int sum = Add(1, 2, so);
}
private static void Run()
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_inceptionGraph = new Emgu.TF.Models.Inception(null, so);
_inceptionGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
_inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
//use a retrained model to recognize followers
_inceptionGraph.Init(
new string[] { "optimized_graph.pb", "output_labels.txt" },
"https://github.com/emgucv/models/raw/master/inception_flower_retrain/",
"Placeholder",
"final_result");
}
/// <summary>
/// Create and run a simple graph that add two numbers and returns the default session devices used.
/// </summary>
/// <returns></returns>
private static Session.Device[] GetSessionDevices()
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
int a = 1;
int b = 1;
//Creating tensor from value a
Tensor tensorA = new Tensor(a);
//Creating tensor from value b
Tensor tensorB = new Tensor(b);
//Create a new graph
Graph graph = new Graph();
//Place holder in the graph for tensorA
Operation opA = graph.Placeholder(DataType.Int32, null, "valA");
//Place holder in the graph for tensorB
Operation opB = graph.Placeholder(DataType.Int32, null, "valB");
//Adding the two tensor
Operation sumOp = graph.Add(opA, opB, "sum");
//Create a new session
using (Session session = new Session(graph, so))
{
//Execute the session and get the sum
Tensor[] results = session.Run(new Output[] { opA, opB }, new Tensor[] { tensorA, tensorB },
new Output[] { sumOp });
Session.Device[] devices = session.ListDevices(null);
return(devices);
}
}
public InceptionPage(Model model)
: base()
{
Title = model == Model.Flower ? "Flower Recognition" : "Object recognition (Inception)";
_model = model;
if (_inceptionGraph == null)
{
SessionOptions so = new SessionOptions();
if (TfInvoke.IsGoogleCudaEnabled)
{
Tensorflow.ConfigProto config = new Tensorflow.ConfigProto();
config.GpuOptions = new Tensorflow.GPUOptions();
config.GpuOptions.AllowGrowth = true;
so.SetConfig(config.ToProtobuf());
}
_inceptionGraph = new Inception(null, so);
_inceptionGraph.OnDownloadProgressChanged += onDownloadProgressChanged;
_inceptionGraph.OnDownloadCompleted += onDownloadCompleted;
_inceptionGraph.OnDownloadCompleted += (sender, e) =>
{
OnButtonClicked(sender, e);
};
}
OnImagesLoaded += (sender, image) =>
{
#if !DEBUG
try
#endif
{
SetMessage("Please wait...");
SetImage();
Tensor imageTensor;
if (_model == Model.Flower)
{
imageTensor = Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(image[0], 299, 299, 0.0f, 1.0f / 255.0f, false, false);
}
else
{
imageTensor =
Emgu.TF.Models.ImageIO.ReadTensorFromImageFile <float>(image[0], 224, 224, 128.0f, 1.0f);
}
Inception.RecognitionResult result;
if (_coldSession)
{
//First run of the recognition graph, here we will compile the graph and initialize the session
//This is expected to take much longer time than consecutive runs.
result = _inceptionGraph.Recognize(imageTensor)[0];
_coldSession = false;
}
//Here we are trying to time the execution of the graph after it is loaded
//If we are not interest in the performance, we can skip the following 3 lines
Stopwatch sw = Stopwatch.StartNew();
result = _inceptionGraph.Recognize(imageTensor)[0];
sw.Stop();
String msg = String.Format("Object is {0} with {1}% probability. Recognized in {2} milliseconds.", result.Label, result.Probability * 100, sw.ElapsedMilliseconds);
SetMessage(msg);
var jpeg = Emgu.Models.NativeImageIO.ImageFileToJpeg(image[0]);
SetImage(jpeg.Raw, jpeg.Width, jpeg.Height);
}
#if !DEBUG
catch (Exception excpt)
{
String msg = excpt.Message.Replace(System.Environment.NewLine, " ");
SetMessage(msg);
}
#endif
};
}
