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[] { 3, 6, 9, 12 })
{
foreach (int outchannels in new int[] { 3, 6, 9, 12 })
{
float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[inchannels * outchannels / 9 * 4]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch), xval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3), wval);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch));
TrivectorDense ope = new TrivectorDense(inchannels, outchannels, gradmode, batch);
ope.Execute(x_tensor, w_tensor, y_tensor);
CollectionAssert.AreEqual(xval, x_tensor.State);
CollectionAssert.AreEqual(wval, w_tensor.State);
y_tensor.CheckOverflow();
Console.WriteLine($"pass: {inchannels},{outchannels},{batch},{gradmode}");
}
}
}
}
}
public void SpeedTest()
{
int inchannels = 33, outchannels = 33;
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels));
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3));
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels));
TrivectorDense ope = new TrivectorDense(inchannels, outchannels);
Stopwatch sw = new Stopwatch();
sw.Start();
ope.Execute(x_tensor, w_tensor, y_tensor);
ope.Execute(x_tensor, w_tensor, y_tensor);
ope.Execute(x_tensor, w_tensor, y_tensor);
ope.Execute(x_tensor, w_tensor, y_tensor);
sw.Stop();
Console.WriteLine($"{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[] { 3, 6, 9, 12 })
{
foreach (int outchannels in new int[] { 3, 6, 9, 12 })
{
float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] wval = (new float[inchannels * outchannels / 9 * 4]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
Trivector[] xcval = (new Trivector[xval.Length / 3])
.Select((_, idx) => new Trivector(xval[idx * 3], xval[idx * 3 + 1], xval[idx * 3 + 2])).ToArray();
Quaternion.Quaternion[] wcval = (new Quaternion.Quaternion[wval.Length / 4])
.Select((_, idx) => new Quaternion.Quaternion(wval[idx * 4], wval[idx * 4 + 1], wval[idx * 4 + 2], wval[idx * 4 + 3])).ToArray();
TrivectorMap0D x = new TrivectorMap0D(inchannels / 3, batch, xcval);
Quaternion.QuaternionFilter0D w = new Quaternion.QuaternionFilter0D(inchannels / 3, outchannels / 3, wcval);
TrivectorMap0D y = Reference(x, w);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch), xval);
OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3), wval);
OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch));
TrivectorDense ope = new TrivectorDense(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}");
}