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);
}
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);
}
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");
}