public static Tensor Deconv2D(Tensor input, Tensor filter, Tensor bias)
{
var channels = filter.Shape[3];
var kernel = "TransConv2D_" + (channels == 3 ? "final" : channels.ToString());
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Up, kernel, input, filter, bias));
}
public static Tensor Conv2D(Tensor input, Tensor filter, Tensor bias)
{
var outChannels = filter.Shape[3];
var kernel = outChannels >= 512 ? "Conv2D_512_1" : "Conv2D_64_8";
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Down, kernel, input, filter, bias));
}
public static Tensor Deconv2D(Tensor input, Tensor filter, Tensor bias)
{
#if ENABLE_COMPUTE
var outChannels = filter.Shape[2];
var kernel = outChannels >= 512 ? "Deconv2D_512_1_1" : "Deconv2D_64_16_1";
if (outChannels == 3)
{
kernel = "Deconv2D_3_256_1";
}
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Backward, kernel, input, filter, bias));
#else
var inHeight = input.Shape[0];
var inWidth = input.Shape[1];
var inChannels = input.Shape[2];
var outHeight = inHeight * 2;
var outWidth = inWidth * 2;
var outChannels = filter.Shape[2];
var filterHeight = filter.Shape[0];
var filterWidth = filter.Shape[1];
var output = new Tensor(new [] { outHeight, outWidth, outChannels });
for (var oc = 0; oc < outChannels; oc++)
{
for (var oy = 0; oy < outHeight; oy++)
{
var ymin = (oy - 1) / 2;
for (var ox = 0; ox < outWidth; ox++)
{
var xmin = (ox - 1) / 2;
var prod = 0.0f;
for (var fy = oy % 2; fy < filterHeight; fy += 2)
{
for (var fx = ox % 2; fx < filterWidth; fx += 2)
{
for (var ic = 0; ic < inChannels; ic++)
{
var pixel = input.Get(ymin + fy / 2, xmin + fx / 2, ic);
var weight = filter.Get(
filterHeight - 1 - fy,
filterWidth - 1 - fx,
oc, ic
);
prod += pixel * weight;
}
}
}
output.Set(oy, ox, oc, prod + bias.Get(oc));
}
}
}
return(output);
#endif
}
public static Tensor Deconv2D(Tensor input, Tensor filter, Tensor bias)
{
var outChannels = filter.Shape[2];
var kernel = outChannels >= 512 ? "TransConv2D_512_1" : "TransConv2D_64_8";
if (outChannels == 3)
{
kernel = "TransConv2D_3_128";
}
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Up, kernel, input, filter, bias));
}
public static Tensor Conv2D(Tensor input, Tensor filter, Tensor bias)
{
#if ENABLE_COMPUTE
var outChannels = filter.Shape[3];
var kernel = outChannels >= 512 ? "Conv2D_512_1_1" : "Conv2D_64_16_1";
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Forward, kernel, input, filter, bias));
#else
var inHeight = input.Shape[0];
var inWidth = input.Shape[1];
var inChannels = input.Shape[2];
var outHeight = inHeight / 2;
var outWidth = inWidth / 2;
var outChannels = filter.Shape[3];
var filterHeight = filter.Shape[0];
var filterWidth = filter.Shape[1];
var output = new Tensor(new [] { outHeight, outWidth, outChannels });
for (var oc = 0; oc < outChannels; oc++)
{
for (var oy = 0; oy < outHeight; oy++)
{
var ymin = oy * 2 - filterHeight / 2 + 1;
for (var ox = 0; ox < outWidth; ox++)
{
var xmin = ox * 2 - filterWidth / 2 + 1;
var prod = 0.0f;
for (var fy = 0; fy < filterHeight; fy++)
{
for (var fx = 0; fx < filterWidth; fx++)
{
for (var ic = 0; ic < inChannels; ic++)
{
var pixel = input.Get(ymin + fy, xmin + fx, ic);
var weight = filter.Get(fy, fx, ic, oc);
prod += pixel * weight;
}
}
}
output.Set(oy, ox, oc, prod + bias.Get(oc));
}
}
}
return(output);
#endif
}
public static Tensor Deconv2D(Tensor input, Tensor filter, Tensor bias)
{
var kernel = "TransConv2D_" + filter.Shape[2];
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Up, kernel, input, filter, bias));
}
public static Tensor Conv2D(Tensor input, Tensor filter, Tensor bias)
{
var kernel = "Conv2D_" + filter.Shape[3];
return(GpuHelper.InvokeConvolutionKernel(GpuHelper.ConvolutionMode.Down, kernel, input, filter, bias));
}
