public override void Forward(SuperArray x)
{
base.Forward(x);
var(n, c, d, h, w) = x.GetConv3DShape();
int pad = 0;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var d_out = (d - PoolSize.Item1) / Strides + 1;
var h_out = (h - PoolSize.Item2) / Strides + 1;
var w_out = (w - PoolSize.Item3) / Strides + 1;
var x_reshaped = x.Reshape(n * c, 1, d, h, w);
//xCols = ImUtil.Im2Col(x_reshaped, PoolSize, pad, Strides);
Output = Ops.ArgMax(xCols);
Output = Output.Reshape(d_out, h_out, w_out, n, c).Transpose(2, 3, 4, 0, 1);
}
public override void Forward(SuperArray x)
{
base.Forward(x);
var(n, c, d, h, w) = x.GetConv3DShape();
Parameter weight = BuildParam("w", new Shape(Filters, c, KernalSize.Item1, KernalSize.Item2, KernalSize.Item2), KernalInitializer, KernalConstraint, KernalRegularizer);
Parameter bias = null;
if (UseBias)
{
bias = BuildParam("b", new Shape(Filters, 1), BiasInitializer, BiasConstraint, BiasRegularizer);
}
int pad = 0;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var d_out = (d - KernalSize.Item1 + 2 * pad) / Strides + 1;
var h_out = (h - KernalSize.Item2 + 2 * pad) / Strides + 1;
var w_out = (w - KernalSize.Item3 + 2 * pad) / Strides + 1;
//xCols = ImUtil.Im2Col(x, KernalSize, pad, Strides);
var wRows = weight.Data.Reshape(Filters, -1);
Output = Ops.Dot(wRows, xCols);
if (UseBias)
{
Output = Output + bias.Data;
}
Output = Output.Reshape(Filters, d_out, h_out, w_out, n).Transpose(4, 0, 1, 2, 3);
}
