nn_fractional_max_pool2d: Applies a 2D fractional max pooling over an input signal...

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Applies a 2D fractional max pooling over an input signal composed of several input planes.

Description

Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham

Usage

nn_fractional_max_pool2d(
  kernel_size,
  output_size = NULL,
  output_ratio = NULL,
  return_indices = FALSE
)

Arguments

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) or a tuple ⁠(kh, kw)⁠
output_size	the target output size of the image of the form ⁠oH x oW⁠. Can be a tuple ⁠(oH, oW)⁠ or a single number oH for a square image ⁠oH x oH⁠
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 TRUE, will return the indices along with the outputs. Useful to pass to nn_max_unpool2d(). Default: FALSE

Details

The max-pooling operation is applied in kH \times kW 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.

Examples

if (torch_is_installed()) {
# pool of square window of size=3, and target output size 13x12
m <- nn_fractional_max_pool2d(3, output_size = c(13, 12))
# pool of square window and target output size being half of input image size
m <- nn_fractional_max_pool2d(3, output_ratio = c(0.5, 0.5))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}

torch documentation built on May 29, 2024, 9:54 a.m.