| LpPool consumes an input blob and
| applies max pooling across the the blob
| according to kernel sizes, stride sizes,
| pad lengths and dilation. $L_p$ pooling
| consists of taking the $L_p$ norm of
| a subset of the input tensor according
| to the kernel size and downsampling
| the data into the output blob for further
| processing.
|
| Pooling layers reduce the spatial dimensionality
| of the input blob. Each of the output
| blob’s dimensions will reduce according
| to:
|
| $$dim_{out}=\frac{dim_{in}-kernel+2*pad}{stride}+1$$
|
| Github Links: - https://github.com/pytorch/pytorch/blob/master/caffe2/operators/lp_pool_op.cc
|