Applies a 3D average pooling over an input signal composed of several input planes.
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}
nn_avg_pool3d( kernel_size, stride = NULL, padding = 0, ceil_mode = FALSE, count_include_pad = TRUE, divisor_override = NULL )
kernel_size |
the size of the window |
stride |
the stride of the window. Default value is |
padding |
implicit zero padding to be added on all three sides |
ceil_mode |
when TRUE, will use |
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 |
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:
a single int – in which case the same value is used for the depth, height and width dimension
a tuple of three ints – in which case, the first int is used for the depth dimension,
the second int for the height dimension and the third int for the width dimension
Input: (N, C, D_{in}, H_{in}, W_{in})
Output: (N, C, D_{out}, H_{out}, W_{out}), where
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
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)
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