private unsafe LearningModelBinding EvaluateContrastAndBrightnessSession(object input, object output)
{
var slope = Math.Tan(ContrastMaxSlider.Value * 3.14159 / 2);
var yintercept = -255 * (ContrastMinSlider.Value * 2 - 1);
if (yintercept < 0)
{
// it was the x-intercept
yintercept = slope * yintercept;
}
var binding = new LearningModelBinding(contrastEffectSession_);
binding.Bind("Input", input);
binding.Bind("Slope", TensorFloat.CreateFromArray(new long[] { 1 }, new float[] { (float)slope }));
binding.Bind("YIntercept", TensorFloat.CreateFromArray(new long[] { 1 }, new float[] { (float)yintercept }));
var outputBindProperties = new PropertySet();
outputBindProperties.Add("DisableTensorCpuSync", PropertyValue.CreateBoolean(true));
binding.Bind("Output", output, outputBindProperties);
EvaluateInternal(contrastEffectSession_, binding);
return(binding);
}
public void SaveState()
{
IPropertySet state = ApplicationData.Current.LocalSettings.Values;
if (state.ContainsKey(AngleKey))
{
state.Remove(AngleKey);
}
if (state.ContainsKey(TrackingKey))
{
state.Remove(TrackingKey);
}
state.Add(AngleKey, PropertyValue.CreateSingle(_rotationY));
state.Add(TrackingKey, PropertyValue.CreateBoolean(_tracking));
}
private LearningModelBinding Evaluate(LearningModelSession session, object input, object output, bool wait = false)
{
// Create the binding
var binding = new LearningModelBinding(session);
// Bind inputs and outputs
string inputName = session.Model.InputFeatures[0].Name;
string outputName = session.Model.OutputFeatures[0].Name;
binding.Bind(inputName, input);
var outputBindProperties = new PropertySet();
outputBindProperties.Add("DisableTensorCpuSync", PropertyValue.CreateBoolean(true));
binding.Bind(outputName, output, outputBindProperties);
// Evaluate
EvaluateInternal(session, binding, wait);
return(binding);
}
public static object Parse(AppDataType.Type type, string s)
{
object obj = null;
switch (type)
{
case AppDataType.Type.Empty: obj = PropertyValue.CreateEmpty();; break;
case AppDataType.Type.UInt8: obj = PropertyValue.CreateUInt8(s.IsEmpty() ? (byte)0 : Byte.Parse(s)); break;
case AppDataType.Type.Int16: obj = PropertyValue.CreateInt16(s.IsEmpty() ? (short)0 : Int16.Parse(s)); break;
case AppDataType.Type.UInt16: obj = PropertyValue.CreateUInt16(s.IsEmpty() ? (ushort)0 : UInt16.Parse(s)); break;
case AppDataType.Type.Int32: obj = PropertyValue.CreateInt32(s.IsEmpty() ? 0 : Int32.Parse(s)); break;
case AppDataType.Type.UInt32: obj = PropertyValue.CreateUInt32(s.IsEmpty() ? 0 : UInt32.Parse(s)); break;
case AppDataType.Type.Int64: obj = PropertyValue.CreateInt64(s.IsEmpty() ? 0 : Int64.Parse(s)); break;
case AppDataType.Type.UInt64: obj = PropertyValue.CreateUInt64(s.IsEmpty() ? 0 : UInt64.Parse(s)); break;
case AppDataType.Type.Single: obj = PropertyValue.CreateSingle(s.IsEmpty() ? 0 : Single.Parse(s)); break;
case AppDataType.Type.Double: obj = PropertyValue.CreateDouble(s.IsEmpty() ? 0 : Double.Parse(s)); break;
case AppDataType.Type.Char16: obj = PropertyValue.CreateChar16(Char.Parse(s)); break;
case AppDataType.Type.Boolean: obj = PropertyValue.CreateBoolean(s.IsEmpty() ? false : Boolean.Parse(s)); break;
case AppDataType.Type.String: obj = s; break;
default: break;
}
return(obj);
}
private static LearningModelEvaluationResult Evaluate(LearningModelSession session, object input)
{
// Create the binding
var binding = new LearningModelBinding(session);
// Create an empty output, that will keep the output resources on the GPU
// It will be chained into a the post processing on the GPU as well
var output = TensorFloat.Create();
// Bind inputs and outputs
// For squeezenet these evaluate to "data", and "squeezenet0_flatten0_reshape0"
string inputName = session.Model.InputFeatures[0].Name;
string outputName = session.Model.OutputFeatures[0].Name;
binding.Bind(inputName, input);
var outputBindProperties = new PropertySet();
outputBindProperties.Add("DisableTensorCpuSync", PropertyValue.CreateBoolean(true));
binding.Bind(outputName, output, outputBindProperties);
// Evaluate
return(session.Evaluate(binding, ""));
}