private async void WinML_Button_Click(object sender, Windows.UI.Xaml.RoutedEventArgs e)
{
//var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/mnist.onnx"));
var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/TaxiFareModel.onnx"));
var learningModel = await LearningModel.LoadFromStreamAsync(modelFile);
}
public static async Task<ShuffleNetModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
ShuffleNetModel learningModel = new ShuffleNetModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return learningModel;
}
public async Task InitializeAsync(ModelType modelType, params string[] parameters)
{
var file = await StorageFile.GetFileFromApplicationUriAsync(new Uri(parameters[0]));
model = await LearningModel.LoadFromStreamAsync(file);
session = new LearningModelSession(model);
binding = new LearningModelBinding(session);
}
public static async Task <FER_Emotion_RecognitionModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
FER_Emotion_RecognitionModel learningModel = new FER_Emotion_RecognitionModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <pointilismModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
pointilismModel learningModel = new pointilismModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <Inceptionv3_convertedModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
Inceptionv3_convertedModel learningModel = new Inceptionv3_convertedModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
private static async Task <OnnxModel> CreateFromStreamAsyncHelper(IRandomAccessStreamReference stream)
{
OnnxModel onnxModel = new OnnxModel();
onnxModel.learningModel = await LearningModel.LoadFromStreamAsync(stream);
onnxModel.session = new LearningModelSession(onnxModel.learningModel);
onnxModel.binding = new LearningModelBinding(onnxModel.session);
return(onnxModel);
}
public static async Task <MultiObjectDetectionModelv8Model> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
MultiObjectDetectionModelv8Model learningModel = new MultiObjectDetectionModelv8Model();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model, new LearningModelDevice(LearningModelDeviceKind.DirectXHighPerformance));
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <Model> CreateFromStreamAsync(IRandomAccessStreamReference stream, LearningModelDevice deviceToRunOn)
{
Model learningModel = new Model();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model, deviceToRunOn);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <TinyYoloV1_2Model> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
var learningModel = new TinyYoloV1_2Model();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <SingelObjectApeModelV8Model> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
SingelObjectApeModelV8Model learningModel = new SingelObjectApeModelV8Model();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <RoadSignDetectionMLModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
RoadSignDetectionMLModel learningModel = new RoadSignDetectionMLModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <modelModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
modelModel learningModel = new modelModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model,
new LearningModelDevice(LearningModelDeviceKind.Default));
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <Model> CreateFromOnnxFilenameAsync(string onnxFilename)
{
Model onnxModel = new Model();
IRandomAccessStreamReference modelFileStream = await StorageFile.GetFileFromPathAsync(onnxFilename);
onnxModel.learningModel = await LearningModel.LoadFromStreamAsync(modelFileStream);
onnxModel.session = new LearningModelSession(onnxModel.learningModel);
onnxModel.binding = new LearningModelBinding(onnxModel.session);
return(onnxModel);
}
public static async Task <ScoringModel> CreateFromStreamAsync(IRandomAccessStreamReference stream, bool UseGpu = false)
{
ScoringModel learningModel = new ScoringModel();
learningModel.model = await AsAsync(LearningModel.LoadFromStreamAsync(stream));
var device = new LearningModelDevice(UseGpu ? LearningModelDeviceKind.DirectXHighPerformance : LearningModelDeviceKind.Cpu);
learningModel.session = new LearningModelSession(learningModel.model, device);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <CustomNetworkModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
// Run on the GPU
//var device = new LearningModelDevice(LearningModelDeviceKind.DirectX);
CustomNetworkModel learningModel = new CustomNetworkModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
//learningModel.session = new LearningModelSession(learningModel.model, device);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
public static async Task <classifierModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
classifierModel learningModel = new classifierModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
// Select GPU or another DirectX device to evaluate the model.
LearningModelDevice device = new LearningModelDevice(LearningModelDeviceKind.DirectX);
// Create the evaluation session with the model and device.
learningModel.session = new LearningModelSession(learningModel.model, device);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
/// <summary>
/// init a ML model
/// </summary>
/// <param name="file"></param>
/// <param name="model"></param>
/// <returns></returns>
public async static Task CreateModelAsync(StorageFile file, IMachineLearningModel learningModel, bool _useCPU = false)
{
LearningModelDevice device = null;
if (_useCPU)
{
device = new LearningModelDevice(LearningModelDeviceKind.Default);
}
else
{
device = new LearningModelDevice(LearningModelDeviceKind.DirectXHighPerformance);
}
learningModel.LearningModel = await LearningModel.LoadFromStreamAsync(file);
learningModel.Session = new LearningModelSession(learningModel.LearningModel, device);
learningModel.Binding = new LearningModelBinding(learningModel.Session);
}
public static async Task <MLModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
var device = new LearningModelDevice(LearningModelDeviceKind.Cpu);
var model = new MLModel();
var load = LearningModel.LoadFromStreamAsync(stream);
while (load.Status != Windows.Foundation.AsyncStatus.Completed)
{
Thread.Sleep(100);
}
model._model = load.GetResults();
model._session = new LearningModelSession(model._model, device);
model._binding = new LearningModelBinding(model._session);
return(model);
}
public async Task LoadModelAsync(bool shouldUseGpu, bool resourceLoad)
{
try
{
// Parse labels from label file
var labelsTextAsset = Resources.Load(LabelsFileName) as TextAsset;
using (var streamReader = new StringReader(labelsTextAsset.text))
{
string line = "";
char[] charToTrim = { '\"', ' ' };
while (streamReader.Peek() >= 0)
{
line = streamReader.ReadLine();
line.Trim(charToTrim);
var indexAndLabel = line.Split(':');
if (indexAndLabel.Count() == 2)
{
_labels.Add(indexAndLabel[1]);
}
}
}
#if ENABLE_WINMD_SUPPORT
if (resourceLoad)
{
// Load from Unity Resources via awkward UWP streams and initialize model
using (var modelStream = new InMemoryRandomAccessStream())
{
var dataWriter = new DataWriter(modelStream);
var modelResource = Resources.Load(ModelFileName) as TextAsset;
dataWriter.WriteBytes(modelResource.bytes);
await dataWriter.StoreAsync();
var randomAccessStream = RandomAccessStreamReference.CreateFromStream(modelStream);
_model = await LearningModel.LoadFromStreamAsync(randomAccessStream);
var deviceKind = shouldUseGpu ? LearningModelDeviceKind.DirectXHighPerformance : LearningModelDeviceKind.Cpu;
_session = new LearningModelSession(_model, new LearningModelDevice(deviceKind));
}
}
else
{
try
{
var modelFile = await StorageFile.GetFileFromApplicationUriAsync(
new Uri($"ms-appx:///Data/StreamingAssets/SqueezeNet.onnx"));
_model = await LearningModel.LoadFromStorageFileAsync(modelFile);
var deviceKind = shouldUseGpu ? LearningModelDeviceKind.DirectXHighPerformance : LearningModelDeviceKind.Cpu;
_session = new LearningModelSession(_model, new LearningModelDevice(deviceKind));
}
catch (Exception e)
{
var exceptionStr = e.ToString();
//StatusBlock.text = exceptionStr;
}
}
// Get model input and output descriptions
var inputImageDescription =
_model.InputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Image)
as ImageFeatureDescriptor;
// Check if input is passed as image if not try to interpret it as generic tensor
if (inputImageDescription != null)
{
InputWidth = inputImageDescription.Width;
InputHeight = inputImageDescription.Height;
}
else
{
var inputTensorDescription =
_model.InputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Tensor)
as TensorFeatureDescriptor;
InputWidth = (uint)inputTensorDescription.Shape[3];
InputHeight = (uint)inputTensorDescription.Shape[2];
}
_outputDescription =
_model.OutputFeatures.FirstOrDefault(feature => feature.Kind == LearningModelFeatureKind.Tensor)
as TensorFeatureDescriptor;
#endif
}
catch
{
#if ENABLE_WINMD_SUPPORT
_model = null;
#endif
throw;
}
}
