Applies a 3D fractional max pooling over an input signal composed of several input planes.
Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham
nn_fractional_max_pool3d( kernel_size, output_size = NULL, output_ratio = NULL, return_indices = FALSE )
kernel_size |
the size of the window to take a max over.
Can be a single number k (for a square kernel of k x k x k) or a tuple |
output_size |
the target output size of the image of the form |
output_ratio |
If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1) |
return_indices |
if |
The max-pooling operation is applied in kTxkHxkW regions by a stochastic step size determined by the target output size. The number of output features is equal to the number of input planes.
if (torch_is_installed()) {
# pool of cubic window of size=3, and target output size 13x12x11
m = nn_fractional_max_pool3d(3, output_size=c(13, 12, 11))
# pool of cubic window and target output size being half of input size
m = nn_fractional_max_pool3d(3, output_ratio=c(0.5, 0.5, 0.5))
input = torch_randn(20, 16, 50, 32, 16)
output = m(input)
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