adaptive_avg_quad_pool2d

torch_geopooling.functional.adaptive_avg_quad_pool2d(weight: Tensor, input: Tensor, exterior: Tuple[float, ...], *, training: bool = True, max_terminal_nodes: int | None = None, max_depth: int | None = None, capacity: int | None = None, precision: int | None = None) → adaptive_quad_pool2d

Adaptive average pooling over quadtree decomposition of input 2D coordinates.

See torch_geopooling.nn.AdaptiveAvgQuadPool2d for more details.

Parameters:

• weight – Weights tensor associated with each tile of a quadtree.

• input – Input 2D coordinates as pairs of x (longitude) and y (latitude).

• exterior – Geometrical boundary of the learning space in (X, Y, W, H) format.

• training – True, when executed during training, and False otherwise.

• max_terminal_nodes – Optional maximum number of terminal nodes in a quadtree. Once a maximum is reached, internal nodes are no longer sub-divided and tree stops growing.

• max_depth – Maximum depth of the quadtree. Default: 17.

• capacity – Maximum number of inputs, after which a quadtree’s node is subdivided and depth of the tree grows. Default: 1.

• precision – Optional rounding of the input coordinates. Default: 7.