public void SpeedTest()
{
int inwidth = 512, inheight = 512, channels = 32, ksize = 3, stride = 2;
int outwidth = (inwidth - ksize) / stride + 1, outheight = (inheight - ksize) / stride + 1;
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map2D(channels, inwidth, inheight));
OverflowCheckedTensor gy_tensor = new OverflowCheckedTensor(Shape.Map2D(channels, outwidth, outheight));
OverflowCheckedTensor gw_tensor = new OverflowCheckedTensor(Shape.Kernel2D(channels, 1, ksize, ksize));
ChannelwiseKernelProduct ope = new ChannelwiseKernelProduct(inwidth, inheight, channels, ksize, ksize, stride);
Stopwatch sw = new Stopwatch();
sw.Start();
ope.Execute(x_tensor, gy_tensor, gw_tensor);
ope.Execute(x_tensor, gy_tensor, gw_tensor);
ope.Execute(x_tensor, gy_tensor, gw_tensor);
ope.Execute(x_tensor, gy_tensor, gw_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 channels in new int[] { 1, 2, 3, 4, 5, 10, 15, 20 })
{
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 * channels * batch]).Select((_, idx) => idx * 1e-3f).ToArray();
float[] gyval = (new float[outwidth * outheight * channels * batch]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray();
Map2D x = new Map2D(channels, inwidth, inheight, batch, xval);
Map2D gy = new Map2D(channels, outwidth, outheight, batch, gyval);
Filter2D gw = Reference(x, gy, kwidth, kheight, stride);
OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map2D(channels, inwidth, inheight, batch), xval);
OverflowCheckedTensor gy_tensor = new OverflowCheckedTensor(Shape.Map2D(channels, outwidth, outheight, batch), gyval);
OverflowCheckedTensor gw_tensor = new OverflowCheckedTensor(Shape.Kernel2D(channels, 1, kwidth, kheight));
ChannelwiseKernelProduct ope = new ChannelwiseKernelProduct(inwidth, inheight, channels, kwidth, kheight, stride, batch);
ope.Execute(x_tensor, gy_tensor, gw_tensor);
float[] gw_expect = gw.ToArray();
float[] gw_actual = gw_tensor.State;
CollectionAssert.AreEqual(xval, x_tensor.State);
CollectionAssert.AreEqual(gyval, gy_tensor.State);
AssertError.Tolerance(gw_expect, gw_actual, 1e-7f, 1e-5f, ref max_err, $"mismatch value {channels},{kwidth},{kheight},{stride},{inwidth},{inheight},{batch}");
Console.WriteLine($"pass: {channels},{kwidth},{kheight},{stride},{inwidth},{inheight},{batch}");
}
}
}
}
}
}
}
Console.WriteLine($"maxerr:{max_err}");
}
