conv.py

import torch

from .basis import SplineBasis
from .weighting import SplineWeighting

from .utils.degree import degree as node_degree


def spline_conv(src,
                edge_index,
                pseudo,
                weight,
                kernel_size,
                is_open_spline,
                degree,
                root_weight=None,
                bias=None):
    """Applies the spline-based convolution operator :math:`(f \star g)(i) =
    \frac{1}{|\mathcal{N}(i)|} \sum_{l=1}^{M_{in}} \sum_{j \in \mathcal{N}(i)}
    f_l(j) \cdot g_l(u(i, j))` over several node features of an input graph.
    The kernel function :math:`g_l` is defined over the weighted B-spline
    tensor product basis for a single input feature map :math:`l`.

    Args:
        src (:class:`Tensor`): Input node features of shape
            (number_of_nodes x in_channels).
        edge_index (:class:`LongTensor`): Graph edges, given by source and
            target indices, of shape (2 x number_of_edges) in the fixed
            interval [0, 1].
        pseudo (:class:`Tensor`): Edge attributes, ie. pseudo coordinates,
            of shape (number_of_edges x number_of_edge_attributes).
        weight (:class:`Tensor`): Trainable weight parameters of shape
            (kernel_size x in_channels x out_channels).
        kernel_size (:class:`LongTensor`): Number of trainable weight
            parameters in each edge dimension.
        is_open_spline (:class:`ByteTensor`): Whether to use open or closed
            B-spline bases for each dimension.
        degree (:class:`Scalar`): B-spline basis degree.
        root_weight (:class:`Tensor`, optional): Additional shared trainable
            parameters for each feature of the root node of shape
            (in_channels x out_channels). (default: :obj:`None`)
        bias (:class:`Tensor`, optional): Optional bias of shape
            (out_channels). (default: :obj:`None`)

    :rtype: :class:`Tensor`
    """

    src = src.unsqueeze(-1) if src.dim() == 1 else src
    pseudo = pseudo.unsqueeze(-1) if pseudo.dim() == 1 else pseudo

    row, col = edge_index
    n, m_out = src.size(0), weight.size(2)

    # Weight each node.
    basis, weight_index = SplineBasis.apply(degree, pseudo, kernel_size,
                                            is_open_spline)
    output = SplineWeighting.apply(src[col], weight, basis, weight_index)

    # Perform the real convolution => Convert e x m_out to n x m_out features.
    row_expand = row.unsqueeze(-1).expand_as(output)
    output = src.new_zeros((n, m_out)).scatter_add_(0, row_expand, output)

    # Normalize output by node degree.
    deg = node_degree(row, n, out=src.new_empty(()))
    output /= deg.unsqueeze(-1).clamp(min=1)

    # Weight root node separately (if wished).
    if root_weight is not None:
        output += torch.mm(src, root_weight)

    # Add bias (if wished).
    if bias is not None:
        output += bias

    return output