public override void Forward(SuperArray x)
{
base.Forward(x);
var(n, c, s) = x.GetConv1DShape();
Parameter weight = BuildParam("w", new Shape(Filters, c, KernalSize), 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;
}
KernalSize = (KernalSize - 1) * DilationRate + 1;
var steps_out = (s - KernalSize + 2 * pad) / Strides + 1;
xCols = ImUtil.Im2Col(x, Tuple.Create(KernalSize, 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, steps_out, n);
Output = Output.Transpose(2, 0, 1);
}
public override void Forward(SuperArray x)
{
base.Forward(x);
var(n, c, s) = x.GetConv1DShape();
int pad = 0;
if (Padding == PaddingType.Same)
{
pad = 1;
}
else if (Padding == PaddingType.Full)
{
pad = 2;
}
var s_out = (s - PoolSize) / Strides + 1;
var x_reshaped = x.Reshape(n * c, 1, s);
xCols = ImUtil.Im2Col(x_reshaped, Tuple.Create(PoolSize, PoolSize), pad, Strides);
Output = Ops.Mean(xCols, 0);
Output = Output.Reshape(s_out, n, c).Transpose(2, 0, 1);
}