How to convert a pytorch tensor of ints to a tensor of booleans?

PyTorch's to(dtype) method has convenient data-type named aliases. You can simply call bool:

>>> t.bool()
tensor([False,  True, False,  True])

>>> t.bool().int()
tensor([0, 1, 0, 1], dtype=torch.int32)

What you're looking for is to generate a boolean mask for the given integer tensor. For this, you can simply check for the condition: "whether the values in the tensor are greater than 0" using simple comparison operator (>) or using torch.gt(), which would then give us the desired result.

# input tensor
In [76]: t   
Out[76]: tensor([ 0, 10,  0, 16])

# generate the needed boolean mask
In [78]: t > 0      
Out[78]: tensor([0, 1, 0, 1], dtype=torch.uint8)

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# sanity check
In [93]: mask = t > 0      

In [94]: mask.type()      
Out[94]: 'torch.ByteTensor'

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Note: In PyTorch version 1.4+, the above operation would return 'torch.BoolTensor'

In [9]: t > 0  
Out[9]: tensor([False,  True, False,  True])

# alternatively, use `torch.gt()` API
In [11]: torch.gt(t, 0)
Out[11]: tensor([False,  True, False,  True])

If you indeed want single bits (either 0s or 1s), cast it using:

In [14]: (t > 0).type(torch.uint8)   
Out[14]: tensor([0, 1, 0, 1], dtype=torch.uint8)

# alternatively, use `torch.gt()` API
In [15]: torch.gt(t, 0).int()
Out[15]: tensor([0, 1, 0, 1], dtype=torch.int32)

The reason for this change has been discussed in this feature-request issue: issues/4764 - Introduce torch.BoolTensor ...

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TL;DR: Simple one liner

t.bool().int()