public void OverflowTest()
{
foreach (bool gradmode in new bool[] { false, true })
{
foreach (int batch in new int[] { 1, 2, 3 })
{
foreach (int inchannels in new int[] { 2, 4, 10, 20 })
{
foreach (int outchannels in new int[] { 6, 14 })
{
float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[inchannels * outchannels / 2]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch), yval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels, outchannels / 2), wval);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch));
ComplexTransposeDense ope = new ComplexTransposeDense(outchannels, inchannels, gradmode, batch);
ope.Execute(y_tensor, w_tensor, x_tensor);
CollectionAssert.AreEqual(yval, y_tensor.State);
CollectionAssert.AreEqual(wval, w_tensor.State);
x_tensor.CheckOverflow();
Console.WriteLine($"pass: {inchannels},{outchannels},{batch},{gradmode}");
}
}
}
}
}
public void SpeedTest()
{
int inchannels = 32, outchannels = 32;
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels));
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels, outchannels / 2));
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels));
ComplexTransposeDense ope = new ComplexTransposeDense(outchannels, inchannels);
ope.Execute(y_tensor, w_tensor, x_tensor);
Stopwatch sw = new Stopwatch();
sw.Start();
ope.Execute(y_tensor, w_tensor, x_tensor);
ope.Execute(y_tensor, w_tensor, x_tensor);
ope.Execute(y_tensor, w_tensor, x_tensor);
ope.Execute(y_tensor, w_tensor, x_tensor);
sw.Stop();
Console.WriteLine($"{sw.ElapsedMilliseconds / 4} msec");
}
public void ExecuteTest()
{
float max_err = 0;
foreach (int batch in new int[] { 1, 2, 3 })
{
foreach (int inchannels in new int[] { 2, 4, 10, 20 })
{
foreach (int outchannels in new int[] { 6, 14 })
{
float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[inchannels * outchannels / 2]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
System.Numerics.Complex[] ycval = (new System.Numerics.Complex[yval.Length / 2])
.Select((_, idx) => new System.Numerics.Complex(yval[idx * 2], yval[idx * 2 + 1])).ToArray();
System.Numerics.Complex[] wcval = (new System.Numerics.Complex[wval.Length / 2])
.Select((_, idx) => new System.Numerics.Complex(wval[idx * 2], wval[idx * 2 + 1])).ToArray();
ComplexMap0D y = new ComplexMap0D(outchannels / 2, batch, ycval);
ComplexFilter0D w = new ComplexFilter0D(inchannels / 2, outchannels / 2, wcval);
ComplexMap0D x = Reference(y, w);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch), yval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels, outchannels / 2), wval);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch));
ComplexTransposeDense ope = new ComplexTransposeDense(outchannels, inchannels, gradmode: false, batch);
ope.Execute(y_tensor, w_tensor, x_tensor);
float[] x_expect = x.ToArray();
float[] x_actual = x_tensor.State;
CollectionAssert.AreEqual(yval, y_tensor.State);
CollectionAssert.AreEqual(wval, w_tensor.State);
AssertError.Tolerance(x_expect, x_actual, 1e-7f, 1e-5f, ref max_err, $"mismatch value {inchannels},{outchannels},{batch}");
Console.WriteLine($"pass: {inchannels},{outchannels},{batch}");
}
}
}
Console.WriteLine($"maxerr:{max_err}");
}
