问题
TL.DR. Is there a 3-dimensional friendly implementation of theano.tensor.nnet.neighbours.images2neibs?
I would like to perform voxel-wise classification of a volume (NxNxN) using a neural network that takes in a nxnxn image, where N>n. To classify each voxel in the volume, I have to iterate through each voxel. For each iterration, I obtain and pass the neighborhood voxels as the input to the neural network. This is simply a sliding window operation, which the operation is the neural network.
While my neural network is implemented in Theano, the sliding window implementation is in python/numpy. Since this is not a pure Theano operation, the classification takes forever (> 3 hours) to classify all voxels in one volume. For 2d sliding window operation, Theano has a helper method, theano.tensor.nnet.neighbours.images2neibs, is there a similar implementation for 3-dimensional images?
Edit:
There are existing numpy solutions (1 and 2) for n-d sliding window, both use np.lib.stride_tricks.as_strided to provide "views of the sliding window", thus preventing memory issues. In my implementation, the sliding window arrays are being passed from numpy (Cython) to Python and then to Theano. To boost performance, it's likely I have to bypass Python.
回答1:
Eickenberg and Kastner's OverfeatTransformer utility in sklearn_theano.feature_extraction.overfeat would be a good match for this operation, as mentioned by OP.
来源：https://stackoverflow.com/questions/35691947/3d-sliding-window-operation-in-theano