public static Tensor conv2d(Tensor x, Tensor weight, Tensor bias = null, Union <int, Tuple <int, int> > stride = null, Union <int, Tuple <int, int> > padding = null, Union <int, Tuple <int, int> > dilation = null, int groups = 1)
{
if (x.dtype == torch.@bool)
{
throw new TorchException("TorchException: nn.functional.conv2d is not implemented for bool tensors.");
}
if (x.__shape.Length != 4)
{
throw new TorchException("TorchException: nn.functional.conv2d requires 4D input, but " + x.__shape.Length.ToString() + "D given.");
}
Tuple <int, int> stride__;
if (stride == null)
{
stride__ = new Tuple <int, int>(1, 1);
}
else
{
if ((Tuple <int, int>)stride != null)
{
stride__ = (Tuple <int, int>)stride;
}
else
{
stride__ = new Tuple <int, int>((int)stride, (int)stride);
}
}
Tuple <int, int> padding__;
if (padding == null)
{
padding__ = new Tuple <int, int>(0, 0);
}
else
{
if ((Tuple <int, int>)padding != null)
{
padding__ = (Tuple <int, int>)padding;
}
else
{
padding__ = new Tuple <int, int>((int)padding, (int)padding);
}
}
Tuple <int, int> dilation__;
if (dilation == null)
{
dilation__ = new Tuple <int, int>(1, 1);
}
else
{
if ((Tuple <int, int>)dilation != null)
{
dilation__ = (Tuple <int, int>)dilation;
}
else
{
dilation__ = new Tuple <int, int>((int)dilation, (int)dilation);
}
}
if ((stride__.Item1 < 1) || (stride__.Item2 < 1))
{
throw new TorchException("TorchException: stride should be >= 1.");
}
if ((padding__.Item1 < 0) || (padding__.Item2 < 0))
{
throw new TorchException("TorchException: padding should be >= 0.");
}
if ((dilation__.Item1 < 1) || (dilation__.Item2 < 1))
{
throw new TorchException("TorchException: dilation should be >= 1.");
}
int srcB = x.__shape[0];
int srcC = x.__shape[1];
int srcH = x.__shape[2];
int srcW = x.__shape[3];
int padX = padding__.Item2;
int padY = padding__.Item1;
int padH = padding__.Item1;
int padW = padding__.Item2;
int dilationX = dilation__.Item2;
int dilationY = dilation__.Item1;
int strideX = stride__.Item2;
int strideY = stride__.Item1;
int kernelX = weight.__shape[3];
int kernelY = weight.__shape[2];
int dstC = weight.__shape[0];
int dstH = (srcH + padY + padH - (dilationY * (kernelY - 1) + 1)) / strideY + 1;
int dstW = (srcW + padX + padW - (dilationX * (kernelX - 1) + 1)) / strideX + 1;
switch (x.dtype)
{
case torch.float32:
{
switch (weight.dtype)
{
case torch.float32:
{
var y = new Tensor(new int[] { srcB, dstC, dstH, dstW }, torch.float32, (!torch.autograd.grad_mode.no_grad.prev) && (x.requires_grad || weight.requires_grad));
MKL.Conv2d(x.__float, srcB, srcC, srcH, srcW, kernelY, kernelX, dilationY, dilationX, strideY, strideX, padY, padX, padH, padW, groups, weight.__float, y.__float, dstC, dstH, dstW);
if ((object)bias != null)
{
y = y + torch.unsqueeze(torch.unsqueeze(torch.unsqueeze(bias, 0), 2), 3);
}
return(y);
}
}
break;
}
}
return(null);
}
