torch_conv2d: Conv2d

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Conv2d

Description

Conv2d

Usage

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)
}

torch documentation built on June 7, 2023, 6:19 p.m.