Converting Between EagerPy and Native Tensors

A native tensor could be a PyTorch GPU or CPU tensor, a TensorFlow tensor, a JAX array, or a NumPy array.

A native PyTorch tensor:

import torch
x = torch.tensor([1., 2., 3., 4., 5., 6.])

A native TensorFlow tensor:

import tensorflow as tf
x = tf.constant([1., 2., 3., 4., 5., 6.])

A native JAX array:

import jax.numpy as np
x = np.array([1., 2., 3., 4., 5., 6.])

A native NumPy array:

import numpy as np
x = np.array([1., 2., 3., 4., 5., 6.])

No matter which native tensor you have, it can always be turned into the appropriate EagerPy tensor using ep.astensor. This will automatically wrap the native tensor with the correct EagerPy tensor class. The original native tensor can always be accessed using the .raw attribute.

# x should be a native tensor (see above)
# for example:
import torch
x = torch.tensor([1., 2., 3., 4., 5., 6.])

# Any native tensor can easily be turned into an EagerPy tensor
import eagerpy as ep
x = ep.astensor(x)

# Now we can perform any EagerPy operation
x = x.square()

# And convert the EagerPy tensor back into a native tensor
x = x.raw
# x will now again be a native tensor (e.g. a PyTorch tensor)

Especially in functions, it is common to convert all inputs to EagerPy tensors. This could be done using individual calls to ep.astensor, but using ep.astensors this can be written even more compactly.

# x, y should be a native tensors (see above)
# for example:
import torch
x = torch.tensor([1., 2., 3.])
y = torch.tensor([4., 5., 6.])

import eagerpy as ep
x, y = ep.astensors(x, y)  # works for any number of inputs