torch_conv3d function

Conv3d

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)
}