[matlab]vl_nnpool

%VL_NNPOOL CNN poolinng.%   Y = VL_NNPOOL(X, POOL) applies the pooling operator to all%   channels of the data X using a square filter of size POOL. X is a%   SINGLE array of dimension H x W x D x N where (H,W) are the%   height and width of the map stack, D is the image depth (number%   of feature channels) and N the number of of images in the stack.%%   Y = VL_NNPOOL(X, [POOLY, POOLX]) uses a rectangular filter of%   height POOLY and width POOLX.%%   DZDX = VL_NNPOOL(X, POOL, DZDY) computes the derivatives of the%   block projected onto DZDY. DZDX and DZDY have the same dimensions%   as X and Y respectively.%%   VL_NNPOOL(..., 'option', value, ...) takes the following options:%%   `Stride`:: 1%     The output stride (downsampling factor). It can be either a%     scalar for isotropic downsampling or a vector [STRIDEY%     STRIDEX].%%   `Pad`:: 0%     The amount of input padding. Input images are padded with zeros%     by this number of pixels on all sides before the convolution is%     computed. It can also be a vector [TOP BOTTOM LEFT RIGHT] to%     specify a different amount of padding in each direction. The%     size of the pooling filter has to exceed the padding.%%   `Method`:: 'max'%     Specify method of pooling. It can be either 'max' (retain max value%     over the pooling region per channel) or 'avg' (compute the average%     value over the pooling region per channel).%%   The pooling window must be not larger than the padded image, i.e.%%     1 <= POOLY <= HEIGHT + (PADTOP + PADBOTTOM),%     1 <= POOLX <= WIDTH + (PADLEFT + PADRIGHT).%%   The output a is a SINGLE array of dimension YH x YW x K x N of N%   images with K challens and size:%%     YH = floor((H + (PADTOP+PADBOTTOM) - POOLY)/STRIDEY) + 1,%     YW = floor((W + (PADLEFT+PADRIGHT) - POOLX)/STRIDEX) + 1.%%   The derivative DZDY has the same dimension of the output Y and%   the derivative DZDX has the same dimension as the input X.%%   ## CUDNN SUPPORT%%   If compiled in, the function will use cuDNN convolution routines%   (with the exception of asymmetric left-right or top-bottom%   padding and average pooling that triggers a bug in cuDNN). You%   can use the 'NoCuDNN' option to disable cuDNN or 'cuDNN' to%   activate it back again (the choice sticks until MATLAB purges the%   MEX files for any reason).% Copyright (C) 2014 Andrea Vedaldi, Karel Lenc, and Max Jaderberg.% Copyright (C) 2015 Andrea Vedaldi and Karel Lenc.% All rights reserved.%% This file is part of the VLFeat library and is made available under% the terms of the BSD license (see the COPYING file).

几种调用方式：
Y = VL_NNPOOL(X, POOL)
正方形核池化。
input：X（HxWxDxN）D为feature通道数
kernel size：POOL
Y = VL_NNPOOL(X, [POOLY, POOLX])
长方形核池化。
input：X，同上
kernel size：POOLY(h) x POOLX(w)

DZDX = VL_NNPOOL(X, POOL, DZDY)
计算该块投射在dzdy上的导数。
input：X
DZDX and DZDY 分别和X,Y有相同的维度