Source code for torch_geometric.nn.conv.feast_conv

from typing import Union

import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import normal
from torch_geometric.typing import Adj, PairTensor, SparseTensor, torch_sparse
from torch_geometric.utils import add_self_loops, remove_self_loops

[docs]class FeaStConv(MessagePassing): r"""The (translation-invariant) feature-steered convolutional operator from the `"FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis" <>`_ paper. .. math:: \mathbf{x}^{\prime}_i = \frac{1}{|\mathcal{N}(i)|} \sum_{j \in \mathcal{N}(i)} \sum_{h=1}^H q_h(\mathbf{x}_i, \mathbf{x}_j) \mathbf{W}_h \mathbf{x}_j with :math:`q_h(\mathbf{x}_i, \mathbf{x}_j) = \mathrm{softmax}_j (\mathbf{u}_h^{\top} (\mathbf{x}_j - \mathbf{x}_i) + c_h)`, where :math:`H` denotes the number of attention heads, and :math:`\mathbf{W}_h`, :math:`\mathbf{u}_h` and :math:`c_h` are trainable parameters. Args: in_channels (int): Size of each input sample, or :obj:`-1` to derive the size from the first input(s) to the forward method. out_channels (int): Size of each output sample. heads (int, optional): Number of attention heads :math:`H`. (default: :obj:`1`) add_self_loops (bool, optional): If set to :obj:`False`, will not add self-loops to the input graph. (default: :obj:`True`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F_{in})` or :math:`((|\mathcal{V_s}|, F_{in}), (|\mathcal{V_t}|, F_{in}))` if bipartite, edge indices :math:`(2, |\mathcal{E}|)` - **output:** node features :math:`(|\mathcal{V}|, F_{out})` or :math:`(|\mathcal{V_t}|, F_{out})` if bipartite """ def __init__(self, in_channels: int, out_channels: int, heads: int = 1, add_self_loops: bool = True, bias: bool = True, **kwargs): kwargs.setdefault('aggr', 'mean') super().__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels self.heads = heads self.add_self_loops = add_self_loops self.lin = Linear(in_channels, heads * out_channels, bias=False, weight_initializer='uniform') self.u = Linear(in_channels, heads, bias=False, weight_initializer='uniform') self.c = Parameter(torch.empty(heads)) if bias: self.bias = Parameter(torch.empty(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() self.lin.reset_parameters() self.u.reset_parameters() normal(self.c, mean=0, std=0.1) normal(self.bias, mean=0, std=0.1)
[docs] def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj) -> Tensor: if isinstance(x, Tensor): x = (x, x) if self.add_self_loops: if isinstance(edge_index, Tensor): edge_index, _ = remove_self_loops(edge_index) edge_index, _ = add_self_loops(edge_index, num_nodes=x[1].size(0)) elif isinstance(edge_index, SparseTensor): edge_index = torch_sparse.set_diag(edge_index) # propagate_type: (x: PairTensor) out = self.propagate(edge_index, x=x) if self.bias is not None: out = out + self.bias return out
def message(self, x_i: Tensor, x_j: Tensor) -> Tensor: q = self.u(x_j - x_i) + self.c # Translation invariance. q = F.softmax(q, dim=1) x_j = self.lin(x_j).view(x_j.size(0), self.heads, -1) return (x_j * q.view(-1, self.heads, 1)).sum(dim=1) def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels}, heads={self.heads})')