nn_avg_pool3d: Applies a 3D average pooling over an input signal composed of...

Description Usage Arguments Details Shape Examples

Description

In the simplest case, the output value of the layer with input size (N, C, D, H, W), output (N, C, D_{out}, H_{out}, W_{out}) and kernel_size (kD, kH, kW) can be precisely described as:

\begin{array}{ll} \mbox{out}(N_i, C_j, d, h, w) = & ∑_{k=0}^{kD-1} ∑_{m=0}^{kH-1} ∑_{n=0}^{kW-1} \\ & \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW} \end{array}

Usage

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nn_avg_pool3d(
  kernel_size,
  stride = NULL,
  padding = 0,
  ceil_mode = FALSE,
  count_include_pad = TRUE,
  divisor_override = NULL
)

Arguments

kernel_size

the size of the window

stride

the stride of the window. Default value is kernel_size

padding

implicit zero padding to be added on all three sides

ceil_mode

when TRUE, will use ceil instead of floor to compute the output shape

count_include_pad

when TRUE, will include the zero-padding in the averaging calculation

divisor_override

if specified, it will be used as divisor, otherwise kernel_size will be used

Details

If padding is non-zero, then the input is implicitly zero-padded on all three sides for padding number of points.

The parameters kernel_size, stride can either be:

Shape

D_{out} = ≤ft\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor

H_{out} = ≤ft\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor

W_{out} = ≤ft\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor

Examples

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if (torch_is_installed()) {
  
# pool of square window of size=3, stride=2
m = nn_avg_pool3d(3, stride=2)
# pool of non-square window
m = nn_avg_pool3d(c(3, 2, 2), stride=c(2, 1, 2))
input = torch_randn(20, 16, 50,44, 31)
output = m(input)

}

torch documentation built on Oct. 7, 2021, 9:22 a.m.