public override void Backward(Tensor outputgrad)
{
uint?pad = null;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var dout_flat = outputgrad.Transpose(3, 0, 1, 2).Reshape(Filters, -1);
var dW = Dot(dout_flat, xCols.Transpose());
dW = dW.Reshape(Params["w"].Data.Shape);
var db = Sum(outputgrad, 0, 2, 3).Reshape(Filters, -1);
var W_flat = Params["w"].Data.Reshape(Filters, -1);
var dX_col = Dot(W_flat.Transpose(), dout_flat);
Input.Grad = ImgUtil.Col2Im(dX_col, Input.Data.Shape, KernalSize, pad, Strides);
Params["w"].Grad = dW;
if (UseBias)
{
Params["b"].Grad = db;
}
}
public override void Backward(Tensor outputgrad)
{
Tensor dX_col = new Tensor(xCols.Allocator, xCols.ElementType, xCols.Shape);
var(n, c, d, h, w) = Input.Data.GetConv3DShape();
Fill(dX_col, 0);
uint?pad = null;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var dout_flat = outputgrad.Transpose(2, 3, 4, 0, 1).Reshape(1, -1);
var dX = ImgUtil.Col2Im(dout_flat, Input.Data.Shape, PoolSize, pad, Strides);
Input.Grad = dX.Reshape(n, c, d, h, w);
}
public Tensor Col2Im(Tensor cols, long[] x_shape, Tuple <int, int> kernalSize, int padding = 1, int stride = 1)
{
return(Out(ImgUtil.Col2Im(In(cols), x_shape, Tuple.Create <uint, uint>((uint)kernalSize.Item1, (uint)kernalSize.Item2), padding, (uint)stride)));
}
