Torch sum subsets of tensor

It is not completely clear, but I cannot use a comment for this, so.

For the first case you have:

t1 = torch.tensor([[1., 1.], [1., 2.], [3., 4.], [1.,2.]]) t1.shape #=> torch.Size([4, 2]) t1 tensor([[1., 1.], [1., 2.], [3., 4.], [1., 2.]]) 

To get the desired output you should reshape:

tr1 = t1.reshape([2, 2, 2]) res1 = torch.sum(tr1, axis = 1) res1.shape #=> torch.Size([2, 2]) res1 tensor([[2., 3.], [4., 6.]]) 

Let's take a tensor with all one elements (torch.ones) for the second case.

t2 = torch.ones((20, 5)) t2.shape #=> torch.Size([20, 5]) t2 tensor([[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]]) 

So, reshaping to get the required (?) result:

tr2 = tensor.reshape((10, 2, 5)) res2 = torch.sum(tr2, axis = 0) res2.shape #=> torch.Size([2, 5]) res2 tensor([[10., 10., 10., 10., 10.], [10., 10., 10., 10., 10.]]) 

Is this what you are looking for?

I am not aware of any off the shelf solution for that.

If having the average is enough you can use nn.AvgPool1d https://pytorch.org/docs/stable/generated/torch.nn.AvgPool1d.html#avgpool1d:

import torch, torch.nn as nn x = torch.rand(batch_size, channels, lenght) pool = nn.AvgPool1D(kernel_size=10, stride=10) avg = pool(x) 

With this solution, just make sure you are averaging the correct dimension.

EDIT I just realized you can get the sum by modifying the last line with avg = pool(x) * kernel_size!

You can also just write your own function that does the summing for you:

import torch def SumWindow(x, window_size, dim): input_split = torch.split(x, window_size, dim) input_sum = [v.sum(dim=dim), for v in input_split] # may be expensive if there are too many tensors out = torch.cat(inptu_sum, dim=dim) return dim