public void SpeedTest() { int inchannels = 33, outchannels = 33; OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels)); OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3)); OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels)); TrivectorTransposeDense ope = new TrivectorTransposeDense(outchannels, inchannels); 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($"{sw.ElapsedMilliseconds / 4} msec"); }
public void OverflowTest() { foreach (bool gradmode in new bool[] { false, true }) { foreach (int batch in new int[] { 1, 2, 3 }) { foreach (int inchannels in new int[] { 3, 6, 9, 12 }) { foreach (int outchannels in new int[] { 3, 6, 9, 12 }) { float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] wval = (new float[inchannels * outchannels / 9 * 4]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch), yval); OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3), wval); OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch)); TrivectorTransposeDense ope = new TrivectorTransposeDense(outchannels, inchannels, gradmode, batch); ope.Execute(y_tensor, w_tensor, x_tensor); CollectionAssert.AreEqual(yval, y_tensor.State); CollectionAssert.AreEqual(wval, w_tensor.State); x_tensor.CheckOverflow(); Console.WriteLine($"pass: {inchannels},{outchannels},{batch},{gradmode}"); } } } } }
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[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] wval = (new float[inchannels * outchannels / 9 * 4]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Trivector[] ycval = (new Trivector[yval.Length / 3]) .Select((_, idx) => new Trivector(yval[idx * 3], yval[idx * 3 + 1], yval[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 y = new TrivectorMap0D(outchannels / 3, batch, ycval); Quaternion.QuaternionFilter0D w = new Quaternion.QuaternionFilter0D(inchannels / 3, outchannels / 3, wcval); TrivectorMap0D x = Reference(y, w); OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch), yval); OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels / 3 * 4, outchannels / 3), wval); OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch)); TrivectorTransposeDense ope = new TrivectorTransposeDense(outchannels, inchannels, 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, $"mismatch value {inchannels},{outchannels},{batch}"); Console.WriteLine($"pass: {inchannels},{outchannels},{batch}"); } } } Console.WriteLine($"maxerr:{max_err}"); }