How to incorporate custom functions into tf.data pipe-lining process for maximum efficiency

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So tf.image for example has some elementary image processing methods already implemented which i'd assumed are optimized. The question is as I'm iterating through a large dataset of images what/how is the recommended way of implementing a more complex function on every image, in batches of course, (for example a a patch 2-D DCT) for it to go as best as possible with the whole tf.data framework.

Thanks in advance.

p.s. of course I could use the "Map" method but i'm asking beyond that. like if I'm passing a pure numpy written function to pass to "map", it wouldn't help as much.

tensorflow pipeline image-processing python-3.x
1 Answers

The current best approach (short of writing custom ops in C++/CUDA) is probably to use https://www.tensorflow.org/api_docs/python/tf/contrib/eager/py_func. This allows you to write any TF eager code and use Python control flow statements. With this you should be able to do most of the things you can do with numpy. The added benefit is that you can use your GPU and the tensors you produce in tfe.py_func will be immediately usable in your regular TF code - no copies are needed.

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