public void SpeedTest()
{
int inwidth = 512, inheight = 512, inchannels = 32, outchannels = 32, ksize = 3, stride = 2;
int outwidth = (inwidth - ksize) / stride + 1, outheight = (inheight - ksize) / stride + 1;
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map2D(inchannels, inwidth, inheight));
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map2D(outchannels, outwidth, outheight));
OverflowCheckedTensor gw_tensor = new OverflowCheckedTensor(Shape.Kernel2D(inchannels, outchannels / 2, ksize, ksize));
ComplexKernelProduct2D ope = new ComplexKernelProduct2D(inwidth, inheight, inchannels, outchannels, ksize, ksize, stride);
Stopwatch sw = new Stopwatch();
sw.Start();
ope.Execute(x_tensor, y_tensor, gw_tensor);
ope.Execute(x_tensor, y_tensor, gw_tensor);
ope.Execute(x_tensor, y_tensor, gw_tensor);
ope.Execute(x_tensor, y_tensor, gw_tensor);
sw.Stop();
Console.WriteLine($"{sw.ElapsedMilliseconds / 4} msec");
}
public void OverflowTest()
{
foreach (bool transpose in new bool[] { false, true })
{
foreach (int batch in new int[] { 1, 2, 3 })
{
foreach (int inchannels in new int[] { 2, 4, 10, 20 })
{
foreach (int outchannels in new int[] { 6, 14 })
{
foreach (int kheight in new int[] { 1, 3, 5 })
{
foreach (int kwidth in new int[] { 1, 3, 5 })
{
foreach (int stride in new int[] { 1, 2, 3 })
{
foreach (int inwidth in new int[] { 8, 9, 13, 17 })
{
foreach (int inheight in new int[] { 8, 9, 19, 23 })
{
int outwidth = (inwidth - kwidth) / stride + 1, outheight = (inheight - kheight) / stride + 1;
float[] xval = (new float[inwidth * inheight * inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] yval = (new float[outwidth * outheight * outchannels * batch]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map2D(inchannels, inwidth, inheight, batch), xval);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map2D(outchannels, outwidth, outheight, batch), yval);
OverflowCheckedTensor gw_tensor = new OverflowCheckedTensor(Shape.Kernel2D(inchannels, outchannels / 2, kwidth, kheight));
ComplexKernelProduct2D ope = new ComplexKernelProduct2D(inwidth, inheight, inchannels, outchannels, kwidth, kheight, stride, transpose, batch);
ope.Execute(x_tensor, y_tensor, gw_tensor);
CollectionAssert.AreEqual(xval, x_tensor.State);
CollectionAssert.AreEqual(yval, y_tensor.State);
gw_tensor.CheckOverflow();
Console.WriteLine($"pass: {inchannels},{outchannels},{kwidth},{kheight},{stride},{inwidth},{inheight},{batch},{transpose}");
}
}
}
}
}
}
}
}
}
}
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 })
{
foreach (int kheight in new int[] { 1, 3, 5 })
{
foreach (int kwidth in new int[] { 1, 3, 5 })
{
foreach (int stride in new int[] { 1, 2, 3 })
{
foreach (int inwidth in new int[] { 8, 9, 13, 17 })
{
foreach (int inheight in new int[] { 8, 9, 19, 23 })
{
int outwidth = (inwidth - kwidth) / stride + 1, outheight = (inheight - kheight) / stride + 1;
float[] xval = (new float[inwidth * inheight * inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] yval = (new float[outwidth * outheight * 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();
ComplexMap2D x = new ComplexMap2D(inchannels / 2, inwidth, inheight, batch, xcval);
ComplexMap2D y = new ComplexMap2D(outchannels / 2, outwidth, outheight, batch, ycval);
ComplexFilter2D gw = Reference(x, y, kwidth, kheight, stride);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map2D(inchannels, inwidth, inheight, batch), xval);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map2D(outchannels, outwidth, outheight, batch), yval);
OverflowCheckedTensor gw_tensor = new OverflowCheckedTensor(Shape.Kernel2D(inchannels, outchannels / 2, kwidth, kheight));
ComplexKernelProduct2D ope = new ComplexKernelProduct2D(inwidth, inheight, inchannels, outchannels, kwidth, kheight, stride, transpose: false, batch);
ope.Execute(x_tensor, y_tensor, gw_tensor);
float[] gw_expect = gw.ToArray();
float[] gw_actual = gw_tensor.State;
CollectionAssert.AreEqual(xval, x_tensor.State);
CollectionAssert.AreEqual(yval, y_tensor.State);
AssertError.Tolerance(gw_expect, gw_actual, 1e-7f, 1e-5f, ref max_err, $"mismatch value {inchannels},{outchannels},{kwidth},{kheight},{stride},{inwidth},{inheight},{batch}");
Console.WriteLine($"pass: {inchannels},{outchannels},{kwidth},{kheight},{stride},{inwidth},{inheight},{batch}");
}
}
}
}
}
}
}
}
Console.WriteLine($"maxerr:{max_err}");
}