nn_adaptive_max_pool2d: Applies a 2D adaptive max pooling over an input signal...

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

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

Description

The output is of size H x W, for any input size. The number of output features is equal to the number of input planes.

Usage

nn_adaptive_max_pool2d(output_size, return_indices = FALSE)

Arguments

output_size	the target output size of the image of the form H x W. Can be a tuple ⁠(H, W)⁠ or a single H for a square image H x H. H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices	if TRUE, will return the indices along with the outputs. Useful to pass to nn_max_unpool2d(). Default: FALSE

Examples

if (torch_is_installed()) {
# target output size of 5x7
m <- nn_adaptive_max_pool2d(c(5, 7))
input <- torch_randn(1, 64, 8, 9)
output <- m(input)
# target output size of 7x7 (square)
m <- nn_adaptive_max_pool2d(7)
input <- torch_randn(1, 64, 10, 9)
output <- m(input)
}

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