public void OptimizeTest() { float max_err = 0; foreach (int batch in new int[] { 1, 2 }) { foreach (int inchannels in new int[] { 1, 2, 3, 4, 5, 10, 15, 20 }) { foreach (int outchannels in new int[] { 7, 13 }) { float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] wval = (new float[inchannels * outchannels]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Map0D y = new Map0D(outchannels, batch, yval); Filter0D w = new Filter0D(inchannels, outchannels, 1, wval); Map0D x = Reference(y, w); Map0D x_optimized = OptimizedReference(y, w); float[] x_expect = x.ToArray(); float[] x_actual = x_optimized.ToArray(); 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}"); }
public void ReferenceTest() { int inchannels = 7, outchannels = 11, batch = 2; float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] wval = (new float[outchannels * inchannels]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Map0D x = new Map0D(inchannels, batch, xval); Filter0D w = new Filter0D(inchannels, outchannels, 1, wval); Map0D y = Reference(x, w); float[] y_expect = { 1.5050e-03f, 1.3580e-03f, 1.2110e-03f, 1.0640e-03f, 9.1700e-04f, 7.7000e-04f, 6.2300e-04f, 4.7600e-04f, 3.2900e-04f, 1.8200e-04f, 3.5000e-05f, 5.0820e-03f, 4.5920e-03f, 4.1020e-03f, 3.6120e-03f, 3.1220e-03f, 2.6320e-03f, 2.1420e-03f, 1.6520e-03f, 1.1620e-03f, 6.7200e-04f, 1.8200e-04f }; float[] y_actual = y.ToArray(); AssertError.Tolerance(y_expect, y_actual, 1e-7f, 1e-5f, $"mismatch value {inchannels},{outchannels},{batch}"); }
public static Map0D OptimizedReference(Map0D y, Filter0D w) { int outchannels = y.Channels, inchannels = w.InChannels, batch = y.Batch; Map0D x = new Map0D(inchannels, batch); for (int th = 0; th < batch; th++) { for (int inch = 0; inch < inchannels; inch++) { double sum = 0; int inmap_idx = outchannels * th; int outmap_idx = inch + inchannels * th; int kernel_idx = inch; for (int outch = 0; outch < outchannels; outch++) { sum += y[inmap_idx] * w[kernel_idx]; inmap_idx++; kernel_idx += inchannels; } x[outmap_idx] = sum; } } return(x); }
public static Map0D OptimizedReference(Map0D x, Filter0D w) { int inchannels = x.Channels, outchannels = w.OutChannels, batch = x.Batch; Map0D y = new Map0D(outchannels, batch); for (int th = 0; th < batch; th++) { int inmap_org = th * inchannels; int outmap_idx = th * outchannels; int kernel_idx = 0; for (int outch = 0; outch < outchannels; outch++) { double sum = 0; int inmap_idx = inmap_org; for (int inch = 0; inch < inchannels; inch++) { sum += x[inmap_idx] * w[kernel_idx]; inmap_idx++; kernel_idx++; } y[outmap_idx] = sum; outmap_idx++; } } return(y); }
public void ExecuteTest() { float max_err = 0; foreach (int batch in new int[] { 1, 2 }) { foreach (int inchannels in new int[] { 1, 2, 3, 4, 5, 10, 15, 20 }) { foreach (int outchannels in new int[] { 7, 13 }) { float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] wval = (new float[inchannels * outchannels]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Map0D y = new Map0D(outchannels, batch, yval); Filter0D w = new Filter0D(inchannels, outchannels, 1, wval); Map0D x = Reference(y, w); OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch), yval); OverflowCheckedTensor w_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels, outchannels), wval); OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch)); TransposeDense ope = new TransposeDense(outchannels, inchannels, 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}"); }
public void ExecuteTest() { float max_err = 0; foreach (int batch in new int[] { 1, 2 }) { foreach (int inchannels in new int[] { 1, 2, 3, 4, 5, 10, 15, 20 }) { foreach (int outchannels in new int[] { 7, 13 }) { float[] xval = (new float[inchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Map0D x = new Map0D(inchannels, batch, xval); Map0D y = new Map0D(outchannels, batch, yval); Filter0D gw = Reference(x, y); OverflowCheckedTensor x_tensor = new OverflowCheckedTensor(Shape.Map0D(inchannels, batch), xval); OverflowCheckedTensor y_tensor = new OverflowCheckedTensor(Shape.Map0D(outchannels, batch), yval); OverflowCheckedTensor gw_tensor = new OverflowCheckedTensor(Shape.Kernel0D(inchannels, outchannels)); KernelProduct ope = new KernelProduct(inchannels, outchannels, batch); ope.Execute(x_tensor, y_tensor, gw_tensor); float[] gw_expect = gw.ToArray(); float[] gw_actual = gw_tensor.State; CollectionAssert.AreEqual(xval, x_tensor.State); CollectionAssert.AreEqual(yval, y_tensor.State); AssertError.Tolerance(gw_expect, gw_actual, 1e-7f, 1e-5f, ref max_err, $"mismatch value {inchannels},{outchannels},{batch}"); Console.WriteLine($"pass: {inchannels},{outchannels},{batch}"); } } } Console.WriteLine($"maxerr:{max_err}"); }
public static Map0D Reference(Map0D y, Filter0D w) { int outchannels = y.Channels, inchannels = w.InChannels, batch = y.Batch; Map0D x = new Map0D(inchannels, batch); for (int th = 0; th < batch; th++) { for (int inch = 0; inch < inchannels; inch++) { double sum = 0; for (int outch = 0; outch < outchannels; outch++) { sum += y[outch, th] * w[inch, outch, 0]; } x[inch, th] = sum; } } return(x); }
public void ReferenceTest() { int inchannels = 7, outchannels = 11, batch = 2; float[] yval = (new float[outchannels * batch]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] wval = (new float[outchannels * inchannels]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Map0D y = new Map0D(outchannels, batch, yval); Filter0D w = new Filter0D(inchannels, outchannels, 1, wval); Map0D x = Reference(y, w); float[] x_expect = { 1.4850e-03f, 1.4300e-03f, 1.3750e-03f, 1.3200e-03f, 1.2650e-03f, 1.2100e-03f, 1.1550e-03f, 6.4460e-03f, 6.2700e-03f, 6.0940e-03f, 5.9180e-03f, 5.7420e-03f, 5.5660e-03f, 5.3900e-03f }; float[] x_actual = x.ToArray(); AssertError.Tolerance(x_expect, x_actual, 1e-7f, 1e-5f, $"mismatch value {inchannels},{outchannels},{batch}"); }
public static Filter0D Reference(Map0D x, Map0D y) { int inchannels = x.Channels, outchannels = y.Channels, batch = x.Batch; Filter0D w = new Filter0D(inchannels, outchannels, 1); for (int inch, outch = 0; outch < outchannels; outch++) { for (inch = 0; inch < inchannels; inch++) { double sum = 0; for (int th = 0; th < batch; th++) { sum += x[inch, th] * y[outch, th]; } w[inch, outch, 0] = sum; } } return(w); }
public static Map0D Reference(Map0D x, Filter0D w) { int inchannels = x.Channels, outchannels = w.OutChannels, batch = x.Batch; Map0D y = new Map0D(outchannels, batch); for (int th = 0; th < batch; th++) { for (int outch = 0; outch < outchannels; outch++) { double sum = 0; for (int inch = 0; inch < inchannels; inch++) { sum += x[inch, th] * w[inch, outch, 0]; } y[outch, th] = sum; } } return(y); }
public void ReferenceTest() { int inchannels = 7, outchannels = 11, batch = 2; float[] xval = (new float[batch * inchannels]).Select((_, idx) => idx * 1e-3f).ToArray(); float[] yval = (new float[batch * outchannels]).Select((_, idx) => idx * 1e-3f).Reverse().ToArray(); Map0D x = new Map0D(inchannels, batch, xval); Map0D y = new Map0D(outchannels, batch, yval); Filter0D gw = Reference(x, y); float[] gw_expect = { 7.0000e-05f, 1.0100e-04f, 1.3200e-04f, 1.6300e-04f, 1.9400e-04f, 2.2500e-04f, 2.5600e-04f, 6.3000e-05f, 9.2000e-05f, 1.2100e-04f, 1.5000e-04f, 1.7900e-04f, 2.0800e-04f, 2.3700e-04f, 5.6000e-05f, 8.3000e-05f, 1.1000e-04f, 1.3700e-04f, 1.6400e-04f, 1.9100e-04f, 2.1800e-04f, 4.9000e-05f, 7.4000e-05f, 9.9000e-05f, 1.2400e-04f, 1.4900e-04f, 1.7400e-04f, 1.9900e-04f, 4.2000e-05f, 6.5000e-05f, 8.8000e-05f, 1.1100e-04f, 1.3400e-04f, 1.5700e-04f, 1.8000e-04f, 3.5000e-05f, 5.6000e-05f, 7.7000e-05f, 9.8000e-05f, 1.1900e-04f, 1.4000e-04f, 1.6100e-04f, 2.8000e-05f, 4.7000e-05f, 6.6000e-05f, 8.5000e-05f, 1.0400e-04f, 1.2300e-04f, 1.4200e-04f, 2.1000e-05f, 3.8000e-05f, 5.5000e-05f, 7.2000e-05f, 8.9000e-05f, 1.0600e-04f, 1.2300e-04f, 1.4000e-05f, 2.9000e-05f, 4.4000e-05f, 5.9000e-05f, 7.4000e-05f, 8.9000e-05f, 1.0400e-04f, 7.0000e-06f, 2.0000e-05f, 3.3000e-05f, 4.6000e-05f, 5.9000e-05f, 7.2000e-05f, 8.5000e-05f, 0.0000e+00f, 1.1000e-05f, 2.2000e-05f, 3.3000e-05f, 4.4000e-05f, 5.5000e-05f, 6.6000e-05f }; float[] gw_actual = gw.ToArray(); AssertError.Tolerance(gw_expect, gw_actual, 1e-7f, 1e-5f, $"mismatch value {inchannels},{outchannels},{batch}"); }