Comments (5)
You can always access the underlying native tensor using the .raw
attribute (see the last line of the example):
# 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)
from eagerpy.
@jonasrauber Does that allow me to convert from tf1 tensors to pytorch tensors?
from eagerpy.
Sorry, seems I have misunderstood your question.
No it doesn't. EagerPy's primary purpose is give you the ability to write code that works with PyTorch and TensorFlow, not to convert data from one to the other.
Having said that, your request is actually something I might be able to add.
from eagerpy.
@jonasrauber It seems to me that this kinda of feature, even tho it's not EagerPy's primary purpose, would give a lot of visibility to the package add attract users. I only learned about EagerPy because I was googling for an answer to my question.
from eagerpy.
True, EagerPy was born at a time when this was technically impossible. Now it should be possible, I think, I just don't have a use case for that myself right now and no one who would pay me for implementing it, so it's not really at the top of my agenda at the moment.
from eagerpy.
Related Issues (20)
- Will it support for SparseTensor (Tensorflow or Pytorch)? HOT 1
- How to Transform a torch tensor to tensorflow tensor HOT 1
- eagerpy not working together with Neural Tangents HOT 3
- Equivalent of `np.diag`? HOT 5
- topk
- Inclusion of probability distributions (scope question) HOT 1
- implementation of `slogdet` in eagerpy HOT 5
- add type conversions [feature request] HOT 4
- Python Scalars Support HOT 3
- Missing support for ep.nonzero() and ep.flatnonzero() HOT 1
- Have a decorator to wrap universal functions ? HOT 6
- Support for @ operator ? HOT 6
- Does a universal function can be compiled in tensorflow? HOT 5
- where method do not works with pytorch
- `index_update` seems very slow for tensorflow backend
- Why restrict cross entropy to 2D inputs only? HOT 1
- TensorFlowTensor.index_update fails for int64/float64 tensors and int/float values
- ValueError: Unknown type: <class 'tuple'>
- sigmoid support HOT 1
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from eagerpy.