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