I have the code below which I want to translate into pytorch. I'm looking for a way to translate np.vectorize to any pytorch way in this case
I need to translate this code to pytorch. The code given below use np.vectorize. I am looking for a pytorch equivalent for this.
class SimplexPotentialProjection(object):
    def __init__(self, potential, inversePotential, strong_convexity_const, precision = 1e-10):
        self.inversePotential = inversePotential
        self.gradPsi = np.vectorize(potential)
        self.gradPsiInverse = np.vectorize(inversePotential)
        self.precision = precision
        self.strong_convexity_const = strong_convexity_const


The doc for numpy.vectorize clearly states that:

The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.

Therefore, in order to convert your numpy code to pytorch you'll simply need apply potential and inversePotential in a loop over their tensor arguments.
However, that might be very inefficient. You would better re-implement your functions to act &quot;natively&quot; in a vectorized manner on tensors.
