does tfdeploy support multiple output in a graph?

In the example, tfdeploy get the result of y1=W1x1+b1 by

result1 = y1.eval({x1: batch})

If I have a graph with two output y2=W2(W1x+b1)+b2, and y3=W3(W1x+b1)+b3, in tensorflow I can use

sess.run([y2, y3])

to get y2 and y3 simutaneously while avoiding redundant computation(of y1=W1x1+b1).

is it possible to do the same thing with tfdeploy? or I have to use two commands like below

result2 = y2.eval({x1: batch})
result3 = y3.eval({x1: batch})

[riga]

Yep.

tensorflow has the advantage to be fully backed by a customized & optimized C++ backend that performs all heavy operations.

tfdeploy, on the other hand, essentially relies on bare numpy operations which sometimes have to be combined to exactly resemble the behavior of tensorflow. Conv and pooling ops are good examples. The drawback is that combinations are implemented and executed in Python. And sometimes even numpy functions aren’t completely backed by equivalent C++ functions, but they use different python calls to achieve the desired functionality.

Concerning the tfdeploy conv and pooling ops: I have one or two ideas that might improve the performance. And maybe it’s worth looking into scipy convolve, but this will also require to do some preprocessing, e.g., to ensure the same padding rules.

[riga]

Hi @ugtony,

yes, this is also possible in tfdeploy, although this feature is quite hidden as there’s no such thing as a session object that can evaluate tensors simultaneously.

Per eval invocation, the intermediate results of all depending tensors and ops are cached.

The actual signature of eval is:

eval(feed_dict=None, _uuid=None)

_uuid is used for caching, initially set when None and passed to all depending eval calls. So all you have to do is:

from uuid import uuid4

...

uuid = uuid4()
result2 = y2.eval({x1: batch}, uuid)
result3 = y3.eval({x1: batch}, uuid)

I only tested this feature, but never had to use it productively, so feedback is appreciated 😉