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);
}
protected override async Task EvaluateAsync(MLModelResult result, VideoFrame inputFrame)
{
// Initialize the input
Squeezenet_oldModelInput input = new Squeezenet_oldModelInput()
{
data = inputFrame
};
// Evaluate the input
Squeezenet_oldModelOutput output = await EvaluateAsync(input, result.CorrelationId);
// Get first label from output
List <float> resultProbabilities = output.softmaxout_1 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;
}
}
}
float probability = topProbabilities[0];
string label = ListOfObjectLabels[topProbabilityLabelIndexes[0]];
result.OutputFeatures = new MLModelOutputFeature[]
{
new MLModelOutputFeature()
{
Label = label, Probability = probability
}
};
}