torch_conv3d: Conv3d

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

Conv3d

Description

Conv3d

Usage

torch_conv3d(
  input,
  weight,
  bias = list(),
  stride = 1L,
  padding = 0L,
  dilation = 1L,
  groups = 1L
)

Arguments

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

conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 3D convolution over an input image composed of several input planes.

See nn_conv3d() for details and output shape.

Examples

if (torch_is_installed()) {

# filters = torch_randn(c(33, 16, 3, 3, 3))
# inputs = torch_randn(c(20, 16, 50, 10, 20))
# nnf_conv3d(inputs, filters)
}

torch documentation built on May 29, 2024, 9:54 a.m.