/// \brief Batched average pooling operation, with optional padding and window stride.
///
/// Average pooling takes as its input an data batch tensor of shape \f$(N,C,d_1,\dots,d_n)\f$ where \f$n > 0\f$, every \f$d_i > 0\f$, and where \f$N\f$ is
/// the batch size, and \f$C > 0\f$ is the number of channels (sometimes called features). The dimensions \f$(d_1,\dots,d_n)\f$ correspond to the shape of
/// an \f$n\f$-dimensional data item in a batch. For example, where \f$n=2\f$, the data may represent a two-dimensional image. It also takes four parameters:
///
/// 1. <i>(the window shape)</i> a size vector \f$(w_1,\dots,w_n)\f$ where every \f$w_i \le d_i\f$; and
/// 2. <i>(the window movement strides, optional)</i> a vector of positive integers \f$(s_1,\dots,s_n)\f$.
/// 3. <i>(the padding below, optional)</i> a vector of positive integers \f$(p_1,\dots,p_n)\f$.
/// 4. <i>(the padding above, optional)</i> a vector of positive integers \f$(q_1,\dots,q_n)\f$.
///
/// The output has the shape \f$(N,C,d'_1,\dots,d'_n)\f$, where \f$d'_n = \lceil \frac{p_i + d_i + q_i - w_i + 1}{s_i} \rceil\f$.
///
/// *In the absence of padding*, given an input data batch tensor \f$T_\textit{in}\f$, the output tensor is defined by the equation
/// *In the presence of padding*, we do not always want to divide by a reciprocal equal to the number of elements in the window, since some of the output points are
/// determined by a window that is partly hanging beyond the edge of the tensor. In this case we can define the output via a few intermediate steps.
///
/// First define the <i>sum tensor</i> \f$T_\textit{sum}\f$, with shape \f$(N,C,d'_1,\dots,d'_n)\f$, as follows.
/// where \f$\textit{val}[a,c,j_1,\dots,j_n] = 1\f$ if for all \f$k\f$, \f$p_k \le j_k < p_k + d_k\f$; else \f$0\f$.
///
/// Finally, define \f$T_\textit{out}\f$ as the result of elementwise dividing \f$T_\textit{sum}\f$ by \f$T_\textit{div}\f$.
/// Note that at positions where \f$T_\textit{div}\f$ is zero, values may be infinity or nan. (This corresponds to a condition where the pooling window is completely
/// out of bounds, encompassing no valid values.)
classAvgPool:publicRequiresTensorViewArgs
{
public:
/// \brief Constructs a batched average pooling operation.
///
/// \param arg The node producing the input data batch tensor.
/// \param window_shape The window shape.
/// \param window_movement_strides The window movement strides.
/// \param padding_below The below-padding shape.
/// \param padding_above The above-padding shape.
/// \param arg The node producing the input data batch tensor.<br>
/// `[d1, dn]`
/// \param window_shape The window shape.<br>
/// `[n]`
/// \param window_movement_strides The window movement strides.<br>
/// `[n]`
/// \param padding_below The below-padding shape.<br>
/// `[n]`
/// \param padding_above The above-padding shape.<br>
/// `[n]`
AvgPool(conststd::shared_ptr<Node>&arg,
constShape&window_shape,
constStrides&window_movement_strides,
...
...
@@ -81,17 +47,22 @@ namespace ngraph
/// \brief Constructs a batched, unpadded average pooling operation (i.e., all padding shapes are set to 0).
///
/// \param arg The node producing the input data batch tensor.
/// \param window_shape The window shape.
/// \param window_movement_strides The window movement strides.
/// \param arg The node producing the input data batch tensor.<br>
/// `[d1, ..., dn]`
/// \param window_shape The window shape.<br>
/// `[n]`
/// \param window_movement_strides The window movement strides.<br>
/// `[n]`
AvgPool(conststd::shared_ptr<Node>&arg,
constShape&window_shape,
constStrides&window_movement_strides);
/// \brief Constructs an unstrided batched convolution operation (i.e., all window movement strides are 1 and all padding shapes are set to 0).
///
/// \param arg The node producing the input data batch tensor.
/// \param window_shape The window shape.
/// \param arg The node producing the input data batch tensor.<br>
