blackhc / tfpyth Goto Github PK

How to support multiple outputs in the module
like
y,z=M(x)

can i use function defined in pytorch, such as nn.Conv2d inTensorFlow? or just operation?

I want to recall a class coding by pytorch in tensorflow, but i just found the way that how to use function.
Is it same when recalling a class? Especially the '__call__' attribution in the class has been overwritten?

Hi. First of all, thanks for writing this library. It saves me a lot of time just wrapping tensorflow code into a new function and using it as is in my pytorch projects.

This is exactly the gist of this issue: I thought we might be able to get the interface a little bit simpler so that you just literally have to wrap it into a new function.

I think it should look something like this:

def tf_function(a, b):
    c = 3 * a + 4 * b * b

    return c

session = tf.compat.v1.Session()
f = tfpyth.wrap_torch_from_tensorflow(
        tf_function, ["a", "b"], session=session
    )
# or simpler
f = tfpyth.wrap_torch_from_tensorflow(
        tf_function, session=session
    ) # automatically creates placeholders for "a" and "b" inside
# or even simpler
f = tfpyth.wrap_torch_from_tensorflow(
        tf_function
    ) # automatically creates placeholders for "a" and "b" and session

a_ = th.tensor(1, dtype=th.float32, requires_grad=True)
b_ = th.tensor(3, dtype=th.float32, requires_grad=True)
x = f(a_, b_)

assert x == 39.0

x.backward()

assert np.allclose((a_.grad, b_.grad), (3.0, 24.0))

Turns out, I already went ahead and added this exact feature. You can check it out -->here<--. I was just wondering a PR would be interesting to persue this feature.

Cheers