public override InternalArray Forward(InternalArray ar1)
{
InternalArray ar = ar1.Clone();
var n = ar1.Shape[0];
var c = ar1.Shape[1];
List <double> data = new List <double>();
int pos0 = 0;
for (int i = 0; i < n; i++)
{
for (int j = 0; j < c; j++)
{
var img = ar.GetNext2dImageFrom4dArray(ref pos0);
for (int z = 0; z < img.Data.Length; z++)
{
img.Data[z] = img.Data[z] < 0 ? (img.Data[z] * Weight.Data[j]) : img.Data[z];
}
data.AddRange(img.Data);
}
}
ar.Data = data.ToArray();
return(ar);
}
public InternalArray ProcessImageOptimized2(InternalArray ar, int hout, int wout, int c, int hin, int win)
{
InternalArray ret = new InternalArray(new int[] { outChannels, hout, wout });
InternalArray[,] filters = new InternalArray[outChannels, c];
int pos0 = 0;
for (int ch = 0; ch < outChannels; ch++)
{
for (int zz = 0; zz < c; zz++)
{
var kernel = Weight.GetNext2dImageFrom4dArray(ref pos0);
var kernel2 = Weight.Get2DImageFrom4DArray(ch, zz);
filters[ch, zz] = kernel;
}
}
int shiftx = padding[0] - kernelSize[0] / 2;
int shifty = padding[1] - kernelSize[1] / 2;
Parallel.For(0, hout, (i) =>
{
var imul = (i) * stride[0] - kernelSize[0] / 2 - shiftx;
var maxi1 = Math.Min((ar.Shape[1] - imul) / dilation[0], kernelSize[0]);
var mini1 = Math.Max((int)Math.Ceiling(-(double)imul / dilation[0]), 0);
Parallel.For(0, wout, (j) =>
{
var jmul = (j) * stride[1] - kernelSize[1] / 2 - shifty;
var minj1 = Math.Max((int)Math.Ceiling(-(double)jmul / dilation[1]), 0);
var maxj1 = Math.Min((ar.Shape[2] - jmul) / dilation[1], kernelSize[1]);
for (int ch = 0; ch < outChannels; ch++)
{
double val = 0;
for (int zz = 0; zz < c; zz++)
{
var kernel = filters[ch, zz];
var offset1 = zz * ar.offsets[0];
int kindex = 0;
for (int i1 = mini1; i1 < maxi1; i1++)
{
var x = imul + i1 * dilation[0];
for (int j1 = minj1; j1 < maxj1; j1++)
{
var y = jmul + j1 * dilation[1];
var index = offset1 + x * ar.offsets[1] + y;
val += kernel.Data[kindex] * ar.Data[index];
kindex++;
}
}
}
ret.Set3D(ch, i, j, val);
}
});
});
//for (int i = 0; i < hout; i++)
{
}
return(ret);
}
