I’m trying to implement the same structure described in this squeeze_excitation paper: https://arxiv.org/pdf/1709.01507.pdf. The first layer of the structure is a global pooling layer across height and width channels. I searched Caffe2 python API to found a suitable operator to do this but didn’t found any.
There is a ReduceMean op, however, written for C++ API. It calculates the mean value across the last dimension of the input blob.
Does anyone know how I can make this work? Thanks in advance!
You looking for:
You can see it in this implementation of that paper: