Pooling for 1D tensor

I am looking for a way to reduce the length of a 1D tensor by applying a pooling operation. How can I do it? If I apply MaxPool1d, I get the error max_pool1d() input tensor must have 2 or 3 dimensions but got 1.

Here is my code:

import numpy as np
import torch

A = np.random.rand(768)
m = nn.MaxPool1d(4,4)
A_tensor = torch.from_numpy(A)
output = m(A_tensor)

Answer

Your initialization is fine, you’ve defined the first two parameters of nn.MaxPool1d: kernel_size and stride. For one-dimensional max-pooling both should be integers, not tuples.

The issue is with your input, it should be two-dimensional (the batch axis is missing):

>>> m = nn.MaxPool1d(4, 4)
>>> A_tensor = torch.rand(1, 768)

Then inference will result in:

>>> output = m(A_tensor)
>>> output.shape
torch.Size([1, 192])