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Is there a Pytorch-internal procedure to detect NaNs in Tensors? Tensorflow has the tf.is_nan and the tf.check_numerics operations ... Does Pytorch have something similar, somewhere? I could not find something like this in the docs...
I am looking specifically for a Pytorch internal routine, since I would like this to happen on the GPU as well as on the CPU. This excludes numpy - based solutions (like np.isnan(sometensor.numpy()).any()) ...
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torch.isnan() in PyTorch returns True for the elements if the element is nan(not a number). Otherwise, it returns False.
Official pytorch losses has a flag called reduce or something similar which allows to return the value of the loss for each element of the batch instead of the average. At that step you can simply remove the NaN element and do a manual average+backprop.
The math. isnan() method checks whether a value is NaN (Not a Number), or not. This method returns True if the specified value is a NaN, otherwise it returns False.
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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) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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