从 PyTorch N 维张量中过滤出 NaN 值

Question

This question is very similar to filtering np.nan values from pytorch in a -Dimensional tensor . The difference is that I want to apply the same concept to tensors of 2 or higher dimensions.

I have a tensor that looks like this:

import torch

tensor = torch.Tensor(
[[1, 1, 1, 1, 1],
 [float('nan'), float('nan'), float('nan'), float('nan'), float('nan')],
 [2, 2, 2, 2, 2]]
)

>>> tensor.shape
>>> [3, 5]

I would like to find the most pythonic / PyTorch way of to filter out (remove) the rows of the tensor which are nan . By filtering this tensor along the first ( 0 th axis) I want to obtain a filtered_tensor which looks like this:

>>> print(filtered_tensor)
>>> torch.Tensor(
[[1, 1, 1, 1, 1],
 [2, 2, 2, 2, 2]]
)
>>> filtered_tensor.shape
>>> [2, 5]

Answer 1

Use PyTorch's isnan() together with any() to slice tensor 's rows using the obtained boolean mask as follows:

filtered_tensor = tensor[~torch.any(tensor.isnan(),dim=1)]

Note that this will drop any row that has a nan value in it. If you want to drop only rows where all values are nan replace torch.any with torch.all .

For an N-dimensional tensor you could just flatten all the dims apart from the first dim and apply the same procedure as above:

#Flatten:
shape = tensor.shape
tensor_reshaped = tensor.reshape(shape[0],-1)
#Drop all rows containing any nan:
tensor_reshaped = tensor_reshaped[~torch.any(tensor_reshaped.isnan(),dim=1)]
#Reshape back:
tensor = tensor_reshaped.reshape(tensor_reshaped.shape[0],*shape[1:])