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deep-learningpytorchpooling
user5965026
user5965026

Reputation: 485

indices in MaxPool2d in pytorch

I am studying the documentation at https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html.

In the parameters section, it states

return_indices – if True, will return the max indices along with the outputs. Useful for torch.nn.MaxUnpool2d later

Could someone explain to me what max indices mean here? I believe it is the indices corresponding to the maximal value. If the maximal value is unique, does that mean only 1 index will be returned?

Upvotes: 0

Views: 3915

Answers (1)

aretor
aretor

Reputation: 2569

I assume you already know how max pooling works. Then, let's print some results to get more insights.

import torch
import torch.nn as nn

pool = nn.MaxPool2d(kernel_size=2, return_indices=True)
input = torch.zeros(1, 1, 4, 4)
input[..., 0, 1] = input[..., 1, 3] = input[..., 2, 2] = input[..., 3, 0] = 1. 
print(input)
output
tensor([[[[0., 1., 0., 0.],
          [0., 0., 0., 1.],
          [0., 0., 1., 0.],
          [1., 0., 0., 0.]]]])

output, indices = pool(input)
print(output)
output
tensor([[[[1., 1.],
          [1., 1.]]]])

print(indices)
output
tensor([[[[ 1,  7],
          [12, 10]]]])

If you stretch the input tensor and make it 1d, you can see that indices contains the positions of each 1 value (the maximum for each window of MaxPool2d). As written in the documentation of torch.nn.MaxPool2d, indices is required for the torch.nn.MaxUnpool2d module:

MaxUnpool2d takes in as input the output of MaxPool2d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero.

Upvotes: 3

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