public async Task <Results> EvaluateAsync(VideoFrame inputFrame)
{
if (model != null)
{
// Create bindings for the input and output buffer
var binding = new LearningModelBindingPreview(model as LearningModelPreview);
var outputVariableList = new List <float>();
binding.Bind(inputImageDescription.Name, inputFrame);
binding.Bind(outputTensorDescription.Name, outputVariableList);
// Process the frame through the model
var results = await model.EvaluateAsync(binding, String.Empty);
var resultProbabilities = results.Outputs[outputTensorDescription.Name] as List <float>;
// return the result
return(new Results()
{
Neutral = resultProbabilities[0],
Happiness = resultProbabilities[1],
Surprise = resultProbabilities[2],
Sadness = resultProbabilities[3],
Anger = resultProbabilities[4],
Disgust = resultProbabilities[5],
Fear = resultProbabilities[6],
Contempt = resultProbabilities[7]
});
}
return(null);
}
/// <summary>
///
/// </summary>
/// <param name="previewFrame"></param>
/// <returns></returns>
private async Task EvaluateVideoFrameAsync(VideoFrame previewFrame)
{
if (previewFrame != null)
{
try
{
IList <string> classLabel = new List <string>();
LearningModelBindingPreview binding = new LearningModelBindingPreview(model as LearningModelPreview);
binding.Bind(inputImageDescription.Name, previewFrame);
binding.Bind(outputMapDescription.Name, prob);
binding.Bind(outputTensorDescription.Name, classLabel);
var stopwatch = Stopwatch.StartNew();
LearningModelEvaluationResultPreview results = await model.EvaluateAsync(binding, string.Empty);
stopwatch.Stop();
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () =>
{
fpsTBlock.Text = $"{1000f / stopwatch.ElapsedMilliseconds,4:f1} fps";
statusTBlock.Text = classLabel[0];
});
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
}
}
}
// This method is executed by the ThreadPoolTimer, it performs the evaluation on a copy of the VideoFrame
private async void EvaluateVideoFrame(ThreadPoolTimer timer)
{
// If a lock is being held, or WinML isn't fully initialized, return
if (!semaphore.Wait(0) || !modelBindingComplete)
{
return;
}
try
{
using (evaluatableVideoFrame)
{
// ************ WinML Evaluate Frame ************ //
Debug.WriteLine($"RelativeTime in Seconds: {evaluatableVideoFrame.RelativeTime?.Seconds}");
await model.EvaluateAsync(binding, "TinyYOLO");
// Remove overlapping and low confidence bounding boxes
filteredBoxes = parser.NonMaxSuppress(parser.ParseOutputs(outputArray.ToArray()), 5, .5F);
Debug.WriteLine(filteredBoxes.Count <= 0 ? $"No Valid Bounding Boxes" : $"Valid Bounding Boxes: {filteredBoxes.Count}");
}
}
catch (Exception ex)
{
Debug.WriteLine($"EvaluateFrameException: {ex}");
}
finally
{
semaphore.Release();
}
}
/// <summary>
/// Evaluate the VideoFrame passed in as arg
/// </summary>
/// <param name="inputFrame"></param>
/// <returns></returns>
private async Task EvaluateVideoFrameAsync(VideoFrame inputFrame)
{
if (inputFrame != null)
{
try
{
// Create bindings for the input and output buffer
LearningModelBindingPreview binding = new LearningModelBindingPreview(_model as LearningModelPreview);
binding.Bind(_inputImageDescription.Name, inputFrame);
binding.Bind(_outputTensorDescription.Name, _outputVariableList);
// Process the frame with the model, and time the operation
modeltime_w.Restart();
LearningModelEvaluationResultPreview results = await _model.EvaluateAsync(binding, "test");
modeltime_w.Stop();
List <float> resultProbabilities = results.Outputs[_outputTensorDescription.Name] as List <float>;
// Find the result of the evaluation in the bound output (the top classes detected with the max confidence)
List <float> topProbabilities = new List <float>()
{
0.0f, 0.0f, 0.0f
};
List <int> topProbabilityLabelIndexes = new List <int>()
{
0, 0, 0
};
for (int i = 0; i < resultProbabilities.Count(); i++)
{
for (int j = 0; j < 3; j++)
{
if (resultProbabilities[i] > topProbabilities[j])
{
topProbabilityLabelIndexes[j] = i;
topProbabilities[j] = resultProbabilities[i];
break;
}
}
}
// Display the result
var runtimemS = modeltime_w.ElapsedMilliseconds;
string message = $"Runtime {runtimemS}mS Predominant objects detected are:";
for (int i = 0; i < 3; i++)
{
message += $"\n\"{ _labels[topProbabilityLabelIndexes[i]]}\" with confidence of { topProbabilities[i]}";
}
StatusBlock.Text = message;
}
catch (Exception ex)
{
StatusBlock.Text = $"error: {ex.Message}";
}
ButtonRun.IsEnabled = true;
}
}
public async Task <Image_RecoModelOutput> EvaluateAsync(Image_RecoModelInput input)
{
var d = input.data.Direct3DSurface.Description;
binding.Bind("data", input.data);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
private async Task EvaluateVideoFrameWithYoloAsync(VideoFrame inputFrame)
{
if (inputFrame != null)
{
try
{
// Create bindings for the input and output buffer
var binding = new LearningModelBindingPreview(model);
// R4 WinML does needs the output pre-allocated for multi-dimensional tensors
var outputArray = new List <float>();
outputArray.AddRange(new float[21125]); // Total size of TinyYOLO output
binding.Bind(inputImageDescription.Name, inputFrame);
binding.Bind(outputTensorDescription.Name, outputArray);
// Process the frame with the model
var stopwatch = Stopwatch.StartNew();
//var results = await model.EvaluateAsync(binding, "TinyYOLO");
var results = await model.EvaluateAsync(binding, "TinyYOLOv2");
stopwatch.Stop();
var resultProbabilities = results.Outputs[outputTensorDescription.Name] as List <float>;
// Use out helper to parse to the YOLO outputs into bounding boxes with labels
boxes = parser.ParseOutputs(resultProbabilities.ToArray(), .3F);
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () =>
{
Duration.Text = $"{1000f / stopwatch.ElapsedMilliseconds,4:f1} fps";
StatusBlock.Text = "TinyYoloModel Evaluation Completed";
});
}
catch (Exception ex)
{
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = $"EvaluateVideoFrameWithYoloAsync Error: {ex.Message}");
}
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => EvaluateImageButton.IsEnabled = true);
}
}
public async Task <CNTKGraphModelOutput> EvaluateAsync(CNTKGraphModelInput input)
{
CNTKGraphModelOutput output = new CNTKGraphModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("Input3", input.Input3);
binding.Bind("Softmax99_Output_0", output.Softmax99_Output_0);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
/// <summary>
/// Evaluate the VideoFrame passed in as arg
/// </summary>
/// <param name="inputFrame"></param>
/// <returns></returns>
private async Task EvaluateVideoFrameAsync(VideoFrame inputFrame)
{
if (inputFrame != null)
{
try
{
// Create bindings for the input and output buffer
LearningModelBindingPreview binding = new LearningModelBindingPreview(_model as LearningModelPreview);
binding.Bind(_inputImageDescription.Name, inputFrame);
binding.Bind(_outputTensorDescription.Name, _outputVariableList);
// Process the frame with the model
LearningModelEvaluationResultPreview results = await _model.EvaluateAsync(binding, "test");
List <float> resultProbabilities = results.Outputs[_outputTensorDescription.Name] as List <float>;
// Find the result of the evaluation in the bound output (the top classes detected with the max confidence)
List <float> topProbabilities = new List <float>()
{
0.0f, 0.0f, 0.0f
};
List <int> topProbabilityLabelIndexes = new List <int>()
{
0, 0, 0
};
for (int i = 0; i < resultProbabilities.Count(); i++)
{
for (int j = 0; j < 3; j++)
{
if (resultProbabilities[i] > topProbabilities[j])
{
topProbabilityLabelIndexes[j] = i;
topProbabilities[j] = resultProbabilities[i];
break;
}
}
}
// Display the result
string message = "Predominant objects detected are:";
for (int i = 0; i < 3; i++)
{
message += $"\n{ _labels[topProbabilityLabelIndexes[i]]} with confidence of { topProbabilities[i]}";
}
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = message);
}
catch (Exception ex)
{
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => StatusBlock.Text = $"error: {ex.Message}");
}
await Dispatcher.RunAsync(CoreDispatcherPriority.Normal, () => ButtonRun.IsEnabled = true);
}
}
public async Task <TinyYOLOModelModelOutput> EvaluateAsync(TinyYOLOModelModelInput input)
{
TinyYOLOModelModelOutput output = new TinyYOLOModelModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("image", input.image);
binding.Bind("grid", output.grid);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <Vgg19ModelOutput> EvaluateAsync(Vgg19ModelInput input)
{
Vgg19ModelOutput output = new Vgg19ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data_0", input.data_0);
binding.Bind("prob_1", output.prob_1);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <MNISTModelOutput> EvaluateAsync(MNISTModelInput input)
{
MNISTModelOutput output = new MNISTModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("Input3", input.Input3);
binding.Bind("Plus214_Output_0", output.Plus214_Output_0);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <Squeezenet_oldModelOutput> EvaluateAsync(Squeezenet_oldModelInput input)
{
Squeezenet_oldModelOutput output = new Squeezenet_oldModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data_0", input.data_0);
binding.Bind("softmaxout_1", output.softmaxout_1);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <MpsnnGraphModelOutput> EvaluateAsync(MpsnnGraphModelInput input)
{
MpsnnGraphModelOutput output = new MpsnnGraphModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(_learningModel);
binding.Bind("image", input.Image);
binding.Bind("grid", output.Grid);
LearningModelEvaluationResultPreview evalResult = await _learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <TinyYoloV2ModelOutput> EvaluateAsync(TinyYoloV2ModelInput input)
{
var output = new TinyYoloV2ModelOutput();
var binding = new LearningModelBindingPreview(_learningModel);
binding.Bind("image", input.Image);
binding.Bind("grid", output.Grid);
var evalResult = await _learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <YOLO2ModelOutput> EvaluateAsync(YOLO2ModelInput input)
{
YOLO2ModelOutput output = new YOLO2ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("input__0", input.input__0);
binding.Bind("output__0", output.output__0);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <_x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelOutput> EvaluateAsync(_x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelInput input)
{
_x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelOutput output = new _x0036_fba5602_x002D_2416_x002D_4582_x002D_8f3b_x002D_931bc0eadc24_c97c9b55_x002D_21f5_x002D_48b8_x002D_9a25_x002D_83f235a0d8d7ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <_x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelOutput> EvaluateAsync(_x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelInput input)
{
_x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelOutput output = new _x0034_7c46b62_x002D_55d4_x002D_418f_x002D_9667_x002D_bca58c105516_df5a0f61_x002D_d714_x002D_4789_x002D_8077_x002D_27f227995765ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelOutput> EvaluateAsync(Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelInput input)
{
Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelOutput output = new Af4dec16_x002D_0900_x002D_4f35_x002D_8d03_x002D_28743433843e_cb36632a_x002D_770d_x002D_4ef8_x002D_a996_x002D_d3b067ff0e11ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelOutput> EvaluateAsync(C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelInput input)
{
C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelOutput output = new C2302845_x002D_023d_x002D_4c61_x002D_9b48_x002D_25d860a0c350_c6ad3001_x002D_dc81_x002D_45fd_x002D_a943_x002D_e7afa90c07f5ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <MemeClassifierModelOutput> EvaluateAsync(MemeClassifierModelInput input)
{
MemeClassifierModelOutput output = new MemeClassifierModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.Data);
binding.Bind("classLabel", output.ClassLabel);
binding.Bind("loss", output.Loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <MobilenetModelOutput> EvaluateAsync(MobilenetModelInput input)
{
MobilenetModelOutput output = new MobilenetModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("prob", output.prob);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <GoogLeNetPlacesModelModelOutput> EvaluateAsync(GoogLeNetPlacesModelModelInput input)
{
GoogLeNetPlacesModelModelOutput output = new GoogLeNetPlacesModelModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("sceneImage", input.sceneImage);
binding.Bind("sceneLabel", output.sceneLabel);
binding.Bind("sceneLabelProbs", output.sceneLabelProbs);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <ModelOutput> EvaluateAsync(VideoFrame videoFrame)
{
ModelOutput output = new ModelOutput(_labels);
LearningModelBindingPreview binding = new LearningModelBindingPreview(_learningModel);
binding.Bind("data", videoFrame);
binding.Bind("classLabel", output.ClassLabel);
binding.Bind("loss", output.Loss);
LearningModelEvaluationResultPreview evalResult = await _learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <_x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelOutput> EvaluateAsync(_x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelInput input)
{
_x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelOutput output = new _x0035_3979b10_x002D_4cb6_x002D_417f_x002D_9f57_x002D_a85d70709fbc_44105f29_x002D_1f46_x002D_48b2_x002D_b0ad_x002D_7d42d639cb20ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <_x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelOutput> EvaluateAsync(_x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelInput input)
{
_x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelOutput output = new _x0039_3deb65e_x002D_dfa1_x002D_49a3_x002D_8a7c_x002D_b802a3a909e1_28f9b6ec_x002D_eae5_x002D_49e6_x002D_8da7_x002D_2b70beb4c498ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelOutput> EvaluateAsync(Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelInput input)
{
Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelOutput output = new Ee7165d4_x002D_31e3_x002D_468e_x002D_ba0c_x002D_565f0b33d377_1bc49a81_x002D_827d_x002D_48b3_x002D_84bf_x002D_3a9555b4c54eModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <ViscoreitanoModelOutput> EvaluateAsync(ViscoreitanoModelInput input)
{
ViscoreitanoModelOutput output = new ViscoreitanoModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <_x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelOutput> EvaluateAsync(_x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelInput input)
{
_x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelOutput output = new _x0035_f11ca47_x002D_2841_x002D_4ece_x002D_af83_x002D_e7bc2b5e8603_fe5629e0_x002D_49e2_x002D_4e9c_x002D_be74_x002D_076aa419dcddModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <InkShapesModelOutput> EvaluateAsync(InkShapesModelInput input)
{
InkShapesModelOutput output = new InkShapesModelOutput(_lossCount);
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}
public async Task <E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelOutput> EvaluateAsync(E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelInput input)
{
E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelOutput output = new E6c82f6e_x002D_c60f_x002D_422a_x002D_97b6_x002D_e0406cba82da_6ed0259c_x002D_001e_x002D_4895_x002D_be7a_x002D_4a930321a307ModelOutput();
LearningModelBindingPreview binding = new LearningModelBindingPreview(learningModel);
binding.Bind("data", input.data);
binding.Bind("classLabel", output.classLabel);
binding.Bind("loss", output.loss);
LearningModelEvaluationResultPreview evalResult = await learningModel.EvaluateAsync(binding, string.Empty);
return(output);
}