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While trying to do ndimage.convolve on big numpy.memmap, exception occurs:

Exception has occurred: _ArrayMemoryError Unable to allocate 56.0 GiB for an array with shape (3710, 1056, 3838) and data type float32

Seems that convolve creates a regular numpy array which won't fit into memory.

Could you tell me please if there is a workaround?

Thank you for any input.

aaxx
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2 Answers2

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Scipy and Numpy often create new arrays to store the output value returned. This temporary array is stored in RAM even when the array is stored on a storage device and accessed with memmap. There is an output parameter to control that in many functions (including ndimage.convolve). However, this does not prevent internal in-RAM temporary arrays to be created (though such array are not very frequent and often not huge). There is not much more you can do if the output parameter is not present or a big internal is created. The only thing to do is to write your own implementation that does not allocate huge in-RAM array. C modules, Cython and Numba are pretty good for this. Note that doing efficient convolutions is far from being simple when the kernel is not trivial and there are many research paper addressing this problem.

Jérôme Richard
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  • This answer is a more general solution than the accepted one, which is specific to image manipulation and requires Dask. I found this helpful for memmap usage in numpy in general. – Aaron Zolnai-Lucas Jan 30 '23 at 12:43
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Instead of running your own implementation, another approach that might work would be to use dask's wrapped ndfilters with a dask array created from the memmap. That way, you can delegate the chunking/out-of-memory-calculation parts to Dask.

I haven't actually done this myself, but I see no reason why it wouldn't work!

Dominik Stańczak
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