Tensor Conv2DWinograd(Tensor X, Tensor K, Tensor B, Tensor O, int[] stride, int[] pad)
{
Assert.AreEqual(X.channels, K.kernelDepth);
Assert.AreEqual(K.kernelCount, B.flatWidth);
Assert.AreEqual(B.flatWidth, B.length);
Assert.AreEqual(stride.Length, 2);
Assert.AreEqual(pad.Length, 4);
// Winograd
// transform kernel
TensorShape Kws = new TensorShape(K.batch + 1, K.height + 1, K.width, K.channels);
var fn_wk = new ComputeFunc(m_Kernels, "KernelWinograd_3x3");
fn_wk.SetTensor("X", K.shape, Pin(K).buffer);
var Kw = Dispatch(fn_wk, Kws, K.kernelCount, X.channels, 1);
var fn_w = new ComputeFunc(m_Kernels, "Conv2DWinograd_2x2_3x3");
SetTensor(fn_w, "X", X);
SetTensor(fn_w, "K", Kw);
SetTensor(fn_w, "B", B);
fn_w.shader.SetInts("_Pad", pad);
var OW = Dispatch(fn_w, O.shape, Kw.kernelCount, IDivC(O.width, 2), IDivC(O.height, 2));
return(OW);
}
public void SetTensor(string name, TensorShape shape, ComputeBuffer buffer, Int64 dataOffset = 0)
{
func.SetTensor(name, shape, buffer, dataOffset);
}