torch_conv2d function

Conv2d

torch_conv2d(
  input,
  weight,
  bias = list(),
  stride = 1L,
  padding = 0L,
  dilation = 1L,
  groups = 1L
)

Arguments

• input: input tensor of shape $(\mbox{minibatch} , \mbox{in\_channels} , iH , iW)$
• weight: filters of shape $(\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)$
• bias: optional bias tensor of shape $(\mbox{out\_channels})$. Default: NULL
• stride: the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1
• padding: implicit paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0
• dilation: the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1
• groups: split input into groups, $\mbox{in\_channels}$ should be divisible by the number of groups. Default: 1

conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 2D convolution over an input image composed of several input planes.

See nn_conv2d() for details and output shape.

Examples

if (torch_is_installed()) {

# With square kernels and equal stride
filters = torch_randn(c(8,4,3,3))
inputs = torch_randn(c(1,4,5,5))
nnf_conv2d(inputs, filters, padding=1)
}