public void ReferenceTest()
{
int inchannels = 6, outchannels = 8, batch = 3;
float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[outchannels * inchannels / 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);
float[] x_expect =
{
-4.000000e-05f, 2.600000e-04f, -3.200000e-05f, 2.040000e-04f, -2.400000e-05f, 1.480000e-04f,
-8.000000e-06f, 1.124000e-03f, 0.000000e+00f, 9.400000e-04f, 8.000000e-06f, 7.560000e-04f,
2.400000e-05f, 1.988000e-03f, 3.200000e-05f, 1.676000e-03f, 4.000000e-05f, 1.364000e-03f,
};
float[] x_actual = x.ToArray();
AssertError.Tolerance(x_expect, x_actual, 1e-9f, 1e-5f, $"mismatch value {inchannels},{outchannels},{batch}");
}
public void ReferenceTest()
{
int inchannels = 6, outchannels = 8, batch = 3;
float[] xval = (new float[batch * inchannels]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[outchannels * inchannels / 2]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
System.Numerics.Complex[] xcval = (new System.Numerics.Complex[xval.Length / 2])
.Select((_, idx) => new System.Numerics.Complex(xval[idx * 2], xval[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 x = new ComplexMap0D(inchannels / 2, batch, xcval);
ComplexFilter0D w = new ComplexFilter0D(inchannels / 2, outchannels / 2, wcval);
ComplexMap0D y = Reference(x, w);
float[] y_expect =
{
-5.400000e-05f, 2.930000e-04f, -3.600000e-05f, 2.030000e-04f, -1.800000e-05f, 1.130000e-04f,
0.000000e+00f, 2.300000e-05f, -3.600000e-05f, 1.031000e-03f, -1.800000e-05f, 7.250000e-04f,
-2.710505e-20f, 4.190000e-04f, 1.800000e-05f, 1.130000e-04f, -1.800000e-05f, 1.769000e-03f,
0.000000e+00f, 1.247000e-03f, 1.800000e-05f, 7.250000e-04f, 3.600000e-05f, 2.030000e-04f,
};
float[] y_actual = y.ToArray();
AssertError.Tolerance(y_expect, y_actual, 1e-9f, 1e-5f, $"mismatch value {inchannels},{outchannels},{batch}");
}
public void ReferenceTest()
{
int inchannels = 6, outchannels = 8, batch = 3;
float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
System.Numerics.Complex[] xcval = (new System.Numerics.Complex[xval.Length / 2])
.Select((_, idx) => new System.Numerics.Complex(xval[idx * 2], xval[idx * 2 + 1])).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();
ComplexMap0D x = new ComplexMap0D(inchannels / 2, batch, xcval);
ComplexMap0D y = new ComplexMap0D(outchannels / 2, batch, ycval);
ComplexFilter0D gw = Reference(x, y);
float[] gw_expect =
{
3.720000000e-04f, -6.300000000e-05f, 5.460000000e-04f, -6.900000000e-05f, 7.200000000e-04f, -7.500000000e-05f,
2.940000000e-04f, -5.700000000e-05f, 4.440000000e-04f, -6.300000000e-05f, 5.940000000e-04f, -6.900000000e-05f,
2.160000000e-04f, -5.100000000e-05f, 3.420000000e-04f, -5.700000000e-05f, 4.680000000e-04f, -6.300000000e-05f,
1.380000000e-04f, -4.500000000e-05f, 2.400000000e-04f, -5.100000000e-05f, 3.420000000e-04f, -5.700000000e-05f,
};
float[] gw_actual = gw.ToArray();
AssertError.Tolerance(gw_expect, gw_actual, 1e-9f, 1e-5f, $"mismatch value {inchannels},{outchannels},{batch}");
}
public static ComplexFilter0D Reference(ComplexMap0D x, ComplexMap0D gy)
{
int inchannels = x.Channels, outchannels = gy.Channels, batch = x.Batch;
ComplexFilter0D w = new ComplexFilter0D(inchannels, outchannels);
Func <System.Numerics.Complex, System.Numerics.Complex, System.Numerics.Complex> mul_grad = (z1, z2) => {
return(new System.Numerics.Complex(z1.Real * z2.Real + z1.Imaginary * z2.Imaginary, z1.Imaginary * z2.Real - z1.Real * z2.Imaginary));
};
for (int inch, outch = 0; outch < outchannels; outch++)
{
for (inch = 0; inch < inchannels; inch++)
{
System.Numerics.Complex sum = 0;
for (int th = 0; th < batch; th++)
{
sum += mul_grad(gy[outch, th], x[inch, th]);
}
w[inch, outch] = sum;
}
}
return(w);
}
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[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[inchannels * outchannels / 2]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
System.Numerics.Complex[] xcval = (new System.Numerics.Complex[xval.Length / 2])
.Select((_, idx) => new System.Numerics.Complex(xval[idx * 2], xval[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 x = new ComplexMap0D(inchannels / 2, batch, xcval);
ComplexFilter0D w = new ComplexFilter0D(inchannels / 2, outchannels / 2, wcval);
ComplexMap0D y = Reference(x, w);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch), xval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels, outchannels / 2), wval);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch));
ComplexDense ope = new ComplexDense(inchannels, outchannels, gradmode: false, batch);
ope.Execute(x_tensor, w_tensor, y_tensor);
float[] y_expect = y.ToArray();
float[] y_actual = y_tensor.State;
CollectionAssert.AreEqual(xval, x_tensor.State);
CollectionAssert.AreEqual(wval, w_tensor.State);
AssertError.Tolerance(y_expect, y_actual, 1e-7f, 1e-5f, ref max_err, $"mismatch value {inchannels},{outchannels},{batch}");
Console.WriteLine($"pass: {inchannels},{outchannels},{batch}");
}
}
}
Console.WriteLine($"maxerr:{max_err}");
}
public static ComplexMap0D Reference(ComplexMap0D y, ComplexFilter0D w)
{
int inchannels = w.InChannels, outchannels = w.OutChannels, batch = y.Batch;
ComplexMap0D x = new ComplexMap0D(inchannels, batch);
for (int th = 0; th < batch; th++)
{
for (int outch = 0; outch < outchannels; outch++)
{
System.Numerics.Complex v = y[outch, th];
for (int inch = 0; inch < inchannels; inch++)
{
x[inch, th] += v * w[inch, outch];
}
}
}
return(x);
}
public static ComplexMap0D Reference(ComplexMap0D x, ComplexFilter0D w)
{
int inchannels = x.Channels, outchannels = w.OutChannels, batch = x.Batch;
ComplexMap0D y = new ComplexMap0D(outchannels, batch);
for (int th = 0; th < batch; th++)
{
for (int outch = 0; outch < outchannels; outch++)
{
System.Numerics.Complex sum = y[outch, th];
for (int inch = 0; inch < inchannels; inch++)
{
sum += x[inch, th] * w[inch, outch];
}
y[outch, th] = sum;
}
}
return(y);
}