public void SpeedTest()
{
int inwidth = 512, inchannels = 32, outchannels = 32, 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 / 2, ksize));
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map1D(inchannels, inwidth));
ComplexDeconvolution1D ope = new ComplexDeconvolution1D(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, 3 })
{
foreach (int inchannels in new int[] { 2, 4, 10, 20 })
{
foreach (int outchannels in new int[] { 6, 14 })
{
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-3f).ToArray();
float[] wval = (new float[kwidth * 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();
ComplexMap1D y = new ComplexMap1D(outchannels / 2, outwidth, batch, ycval);
ComplexFilter1D w = new ComplexFilter1D(inchannels / 2, outchannels / 2, kwidth, wcval);
ComplexMap1D 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 / 2, kwidth), wval);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map1D(inchannels, inwidth, batch));
ComplexDeconvolution1D ope = new ComplexDeconvolution1D(inwidth, outchannels, inchannels, kwidth, stride, 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, $"mismatch value {inchannels},{outchannels},{kwidth},{stride},{inwidth},{batch}");
Console.WriteLine($"pass: {inchannels},{outchannels},{kwidth},{stride},{inwidth},{batch}");
}
}
}
}
}
}
Console.WriteLine($"maxerr:{max_err}");
}
public void ExecuteTest()
{
int inchannels = 4, outchannels = 6, inwidth = 13, kwidth = 3, stride = 2, batch = 7;
VariableField x = new Tensor(Shape.Map1D(inchannels, inwidth, batch));
Layer layer = new ComplexDeconvolution1D(inchannels, outchannels, kwidth, stride, use_bias: true, pad_mode: PaddingMode.Edge, "conv");
Field y = layer.Forward(x);
(Flow flow, Parameters parameters) = Flow.Optimize(y);
flow.Execute();
Assert.AreEqual(2, parameters.Count);
Assert.AreEqual(inchannels, layer.InChannels);
Assert.AreEqual(outchannels, layer.OutChannels);
Assert.AreEqual(kwidth, layer.Width);
}
public void OverflowTest()
{
foreach (bool gradmode in new bool[] { false, true })
{
foreach (int batch in new int[] { 1, 2, 3 })
{
foreach (int inchannels in new int[] { 2, 4, 10, 20 })
{
foreach (int outchannels in new int[] { 6, 14 })
{
foreach (int kwidth in new int[] { 1, 3, 5 })
{
foreach (int stride in new int[] { 1, 2, 3 })
{
foreach (int inwidth in new int[] { 8, 9, 13, 17 })
{
int outwidth = (inwidth - kwidth) / stride + 1;
float[] yval = (new float[outwidth * outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[kwidth * inchannels * outchannels / 2]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map1D(outchannels, outwidth, batch), yval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel1D(inchannels, outchannels / 2, kwidth), wval);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map1D(inchannels, inwidth, batch));
ComplexDeconvolution1D ope = new ComplexDeconvolution1D(inwidth, outchannels, inchannels, kwidth, stride, gradmode, batch);
ope.Execute(y_tensor, w_tensor, x_tensor);
CollectionAssert.AreEqual(yval, y_tensor.State);
CollectionAssert.AreEqual(wval, w_tensor.State);
x_tensor.CheckOverflow();
Console.WriteLine($"pass: {inchannels},{outchannels},{kwidth},{stride},{inwidth},{batch},{gradmode}");
}
}
}
}
}
}
}
}