Max pool gradient fix #5506
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Max pool gradient fix #5506
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Propagate only one value corresponding to the maximum in the input for both max pool grad and max pool grad grad. Summing is still used, since when stride is not equal to 1, one value in the input can be maximum for more than output.
Cudaatomic is necessary as there are no one-to-many mapping.
Remove accumulation for GradGrad since there are only one element which can correspond to the element in ggz
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Fixes #4761
Updates cpu and gpu versions of max pooling to match cudnn behavior. Behavior is updated only for new gpuarray backend. Definition of max pool grad grad is also updated which now propagates only first value corresponding to the maximum (which also matches behavior of MaxPoolRop in #5323).
Note:
Previous implementation was using mapping from input to input to compute gradient (
num_kernelsis size of input). To match cudnn behavior output to input mapping is needed (num_kernelsis size of output). For the latter case, zero initialization is necessary since there are one to many mapping from the output to the input. Additionally, cuda atomics are needed in case of overlapping regions.