Masking Tensor of Same Shape in Pytorch
Given an Array and Mask of Same Shapes, I Want the Masked Output of the Same Shape and Containing 0 Where Mask Is False. for Example, # Input Array Img =...
Given an array and mask of same shapes, I want the masked output of the same shape and containing 0 where mask is False.
For example,
# input array
img = torch.randn(2, 2)
print(img)
# tensor([[0.4684, 0.8316],
# [0.8635, 0.4228]])
print(img.shape)
# torch.Size([2, 2])
# mask
mask = torch.BoolTensor(2, 2)
print(mask)
# tensor([[False, True],
# [ True, True]])
print(mask.shape)
# torch.Size([2, 2])
# expected masked output of shape 2x2
# tensor([[0, 0.8316],
# [0.8635, 0.4228]])
Issue: The masking changes the shape of the output as follows:
#1: shape changed
img[mask]
# tensor([0.8316, 0.8635, 0.4228])
3 Answers
Simply type-cast your boolean mask to an integer mask, followed by float to bring the mask to the same type as in img. Perform element-wise multiplication afterwards.
masked_output = img * mask.int().float()
One of the ways I found to solve it was:
img[mask==False] = 0
or using
img[~mask] = 0
It'll change the img itself.
The most straight forward way would be creating another tensor to handle it.
import torch
def generate_masked_tensor(input, mask, fill=0):
masked_tensor = torch.zeros(input.size()) + fill
masked_tensor[mask] = input[mask]
return masked_tensor
if __name__ == "__main__":
img = torch.randn(2, 2)
mask = torch.tensor([False, True, True, False]).bool().view(2, 2)
masked_img = generate_masked_tensor(img, mask)
print (masked_img)
The output:
tensor([[0.0000, 0.8028],
[1.5411, 0.0000]])
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