Compute maxima and minima of a 4D tensor

Suppose that we have a 4-dimensional tensor, for instance

``````import torch
X = torch.rand(2, 3, 4, 4)
tensor([[[[-0.9951,  1.6668,  1.3140,  1.4274],
[ 0.2614,  2.6442, -0.3041,  0.7337],
[-1.2690,  0.0125, -0.3885,  0.0535],
[ 1.5270, -0.1186, -0.4458,  0.1389]],

[[ 0.9125, -1.2998, -0.4277, -0.2688],
[-1.6917, -0.8855, -0.2784, -0.6717],
[ 1.1417,  0.4574,  0.4803, -1.6637],
[ 0.7322,  0.2654, -0.1525,  1.7285]],

[[ 1.8310, -1.5765,  0.1392,  1.3431],
[-0.6641, -1.5090, -0.4893, -1.4110],
[ 0.5875,  0.7528, -0.6482, -0.2547],
[-2.3133,  0.3888,  2.1428,  0.2331]]]])
``````

I want to compute the maximum and the minimum values of `X` over the dimensions 2 and 3, that is, to compute two tensors of size (2,3,1,1), one for the maximum and one for the minimum values of the 4x4 blocks.

I started by trying to do that with `torch.max()` and `torch.min()`, but I had no luck. I would expect the `dim` argument of the above functions to be able to take tuple values, but it can take only an integer. So I don’t know how to proceed.

However, specifically for the maximum values, I decided to use `torch.nn.MaxPool2d()` with `kernel_size=4` and `stride=4`. This indeed did the job:

``````max_pool = nn.MaxPool2d(kernel_size=4, stride=4)
X_max = max_pool(X)
tensor([[[[2.6442]],
[[1.7285]],
[[2.1428]]]])
``````

But, afaik, there’s no similar layer for “min”-pooling. Could you please help me on how to compute the minima similarly to the maxima?

Thank you.

Based on this answer on stackoverflow:

``````X_max = X.clone()
X_min = X.clone()
for dim in (2, 3):
X_max = torch.max(input=X_max, dim=dim, keepdim=True)[0]
X_min = torch.min(input=X_min, dim=dim, keepdim=True)[0]
``````