You can always leverage the fact that nan != nan
:
>>> x = torch.tensor([1, 2, np.nan]) tensor([ 1., 2., nan.]) >>> x != x tensor([ 0, 0, 1], dtype=torch.uint8)
With pytorch 0.4 there is also torch.isnan
:
>>> torch.isnan(x)
tensor([ 0, 0, 1], dtype=torch.uint8)
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