Apply a function (similar to map) on a tensor?

Hello

I have a vector : (v1,v2,v3,…,vn) = v

For which I have a function f(v_i) = out_i

Is there a simple and clean way to define F such that :
F(v) = (f(v_1),f(v_2),f(v_3),…,f(v_n)) ?

Without using for loops, possibly by parallelizing the computation? As each instance instance of the function f are independant from each other.

Thanks!

Hi Lam!

As far as I am aware, pytorch does not have this kind of “map”
function.

However, pytorch supports many different functions that act
element-wise on tensors (arithmetic, cos(), log(), etc.). If you
can rewrite your function using element-wise torch tensor
operations, your composite function will also act element-wise,
and will do what you want.

Good luck.

K. Frank

Thanks, @KFrank!

Numpy provides a way to vectorize a function. Examples for the same makes it very clear and easy to understand. I am not able to find a similar thing in PyTorch. A reference to any of the following would be really helpful:

  • How to use map() with PyTorch tensors?
  • Is there any API like np.vectorize?

PS: We want to apply a function f on each element of list of tensors.

Thanks!

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For other readers,
I found this function that satisfy this need from this link:

tensor = torch.tensor([[3,5,1,2],[3,1,5,3],[7,5,8,3]],dtype=torch.float)
print(tensor)
tensor.apply_(lambda x: (x+0.2))
print(tensor)
2 Likes