Conv3d

Usage

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

Arguments

input

input tensor of shape $(\text{minibatch},\text{in\_channels},iT,iH,iW)$

weight

filters of shape $(\text{out\_channels},\frac{\text{in\_channels}}{\text{groups}},kT,kH,kW)$

bias

optional bias tensor of shape $(\text{out\_channels})$. 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, $\text{in\_channels}$ 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)
}
#> NULL