VL_NNROIPOOL
 CNN region of interest pooling.
Y = VL_NNROIPOOL(X, ROIS) pools each feature channel in X in
the specified regions of interest ROIS. ROIS is a 5 x K array
containing K regions. Each region has five coordinates [t, u0,
v0, u1, v1]
where u0
, v0
is the upperleft corner of a ROI,
u1
, v1
is the bottomright corner, and t
is the index of the
image that contains the region. Spatial coordiantes start at (1,1),
with u
indexing the horizontal axis and v
the vertical one.
The image indeces ranges from 1 to the number of images stored
in the tensor X.
If X has C feature channels, then the output Y is a 1 x 1 x C x K array, with one image instance per region. Arguments can be SINGLE or DOUBLE and CPU or GPU arrays; however, they must all be of the same type (unless empty).
DZDX = VL_NNROIPOOL(X, ROIS, DZDY) computes the derivative of the layer projected on DZDY with respect to X.
VL_NNROIPOOL(___, 'opt', value, ...) accepts the following options:

Method
['max'
]Choose between
'max'
and'avg'
(average) pooling. 
Subdivisions
[[1 1]
]Specifies the number [SH,SW] of vertical and horizontal tiles of a region. This makes the output a SH x SW x C x K array.

Transform
[1
]Specifies a spatial transformation to apply to region vertices before they are applied to the input tensor. If T is a scalar, then the transformation is a scaling centered at the origin:
u' = T (u  1) + 1, v' = T (v  1) + 1.
If T is a 2D vector, then different scaling factors for the
u
andv
can be specified. Finally, if T is a 2 x 2 matrix, then:u' = T(1,1) u + T(1,2) v + T(1,3), v' = T(2,1) u + T(2,2) v + T(2,3).
Note that only the upperleft and bottomright corners of each rectangular region are transformed. Thus this is mostly useful for axisaligned transformations; the generality of the expression allows, however, to swap
u
andv
, which may be needed to match different conventions for the box coordiantes.
See also: VL_NNPOOL().