public void SpeedTest()
{
int inwidth = 512, inchannels = 31, outchannels = 31, ksize = 3, stride = 2;
int outwidth = (inwidth - ksize) / stride + 1;
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map1D(outchannels, outwidth));
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel1D(inchannels, outchannels, ksize));
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map1D(inchannels, inwidth));
Deconvolution ope = new Deconvolution(inwidth, outchannels, inchannels, ksize, stride);
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 })
{
foreach (int inchannels in new int[] { 1, 2, 3, 4, 5, 10, 15, 20 })
{
foreach (int outchannels in new int[] { 7, 13 })
{
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 })
{
int outwidth = (inwidth - kwidth) / stride + 1;
float[] yval = (new float[outwidth * outchannels * batch]).Select((_, idx) => idx * 1e-4f).ToArray();
float[] wval = (new float[kwidth * inchannels * outchannels]).Select((_, idx) => idx * 1e-4f).Reverse().ToArray();
Map1D y = new Map1D(outchannels, outwidth, batch, yval);
Filter1D w = new Filter1D(inchannels, outchannels, kwidth, wval);
Map1D x = Reference(y, w, inwidth, kwidth, stride);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map1D(outchannels, outwidth, batch), yval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel1D(inchannels, outchannels, kwidth), wval);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map1D(inchannels, inwidth, batch));
Deconvolution ope = new Deconvolution(inwidth, outchannels, inchannels, kwidth, stride, 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, $"mismatch value {inchannels},{kwidth},{stride},{inwidth},{batch}");
Console.WriteLine($"pass: {inchannels},{outchannels},{kwidth},{stride},{inwidth},{batch}");
}
}
}
}
}
}
Console.WriteLine($"maxerr:{max_err}");
}
