static void Main(string[] args)
{
var modelPath = "models/resnet50.model";
var width = 224;
var tensor = JpegToTensor(GetDataPath("datasets/towers/et1.jpg"), width, width);
var model = Sequential.Load(GetDataPath(modelPath));
var prediction1 = model.Predict(tensor).Squeeze();
tensor = JpegToTensor(GetDataPath("datasets/towers/et2.jpg"), width, width);
var prediction2 = model.Predict(tensor).Squeeze();
Console.WriteLine($" Distance to picture of the same object: {TensorUtils.EuclideanDistance(prediction1, prediction2).ToString("F2")}");
tensor = JpegToTensor(GetDataPath("datasets/towers/et3.jpg"), width, width);
prediction2 = model.Predict(tensor).Squeeze();
Console.WriteLine($" Distance to picture of the same object: {TensorUtils.EuclideanDistance(prediction1, prediction2).ToString("F2")}");
tensor = JpegToTensor(GetDataPath("datasets/towers/pt1.jpg"), width, width);
prediction2 = model.Predict(tensor).Squeeze();
Console.WriteLine($" Ddistance to picture of a different object: {TensorUtils.EuclideanDistance(prediction1, prediction2).ToString("F2")}");
}
static void PredictMnist(string modelPath, string xtestPath, string ytestPath = null)
{
var xtest = TensorUtils.Deserialize(File.OpenRead(xtestPath));
xtest = xtest.Cast(DType.Float32);
xtest = Ops.Div(null, xtest, 255f);
// xtest = xtest.Narrow(0, 0, 101);
var model = Sequential.Load(modelPath);
var result = model.Predict(xtest, batchSize: 32);
if (ytestPath == null)
{
return;
}
var ytest = TensorUtils.Deserialize(File.OpenRead(ytestPath));
ytest = ytest.Cast(DType.Float32);
// ytest = ytest.Narrow(0, 0, 101);
ytest = Ops.Argmax(null, ytest, 1).Squeeze();
var t = result.Narrow(0, 0, 11);
// Console.WriteLine(t.Format());
result = Ops.Argmax(null, result, 1).Squeeze();
t = result.Narrow(0, 0, 11);
// Console.WriteLine(t.Format());
double sum = 0.0;
for (var i = 0; i < ytest.Sizes[0]; ++i)
{
sum += (int)ytest.GetElementAsFloat(i) == (int)result.GetElementAsFloat(i) ? 1.0 : 0.0;
}
Console.WriteLine($"Accuracy: {sum / ytest.Sizes[0] * 100}%");
}