Source code for torch_geometric.nn.conv.arma_conv

from typing import Callable, Optional

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

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.inits import glorot, zeros
from torch_geometric.typing import Adj, OptTensor, SparseTensor
from torch_geometric.utils import spmm

[docs]class ARMAConv(MessagePassing): r"""The ARMA graph convolutional operator from the `"Graph Neural Networks with Convolutional ARMA Filters" <>`_ paper. .. math:: \mathbf{X}^{\prime} = \frac{1}{K} \sum_{k=1}^K \mathbf{X}_k^{(T)}, with :math:`\mathbf{X}_k^{(T)}` being recursively defined by .. math:: \mathbf{X}_k^{(t+1)} = \sigma \left( \mathbf{\hat{L}} \mathbf{X}_k^{(t)} \mathbf{W} + \mathbf{X}^{(0)} \mathbf{V} \right), where :math:`\mathbf{\hat{L}} = \mathbf{I} - \mathbf{L} = \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}` denotes the modified Laplacian :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}`. 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 :math:`\mathbf{x}^{(t+1)}`. num_stacks (int, optional): Number of parallel stacks :math:`K`. (default: :obj:`1`). num_layers (int, optional): Number of layers :math:`T`. (default: :obj:`1`) act (callable, optional): Activation function :math:`\sigma`. (default: :meth:`torch.nn.ReLU()`) shared_weights (int, optional): If set to :obj:`True` the layers in each stack will share the same parameters. (default: :obj:`False`) dropout (float, optional): Dropout probability of the skip connection. (default: :obj:`0.`) 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})`, edge indices :math:`(2, |\mathcal{E}|)`, edge weights :math:`(|\mathcal{E}|)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F_{out})` """ def __init__(self, in_channels: int, out_channels: int, num_stacks: int = 1, num_layers: int = 1, shared_weights: bool = False, act: Optional[Callable] = ReLU(), dropout: float = 0., bias: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels self.num_stacks = num_stacks self.num_layers = num_layers self.act = act self.shared_weights = shared_weights self.dropout = dropout K, T, F_in, F_out = num_stacks, num_layers, in_channels, out_channels T = 1 if self.shared_weights else T self.weight = Parameter(torch.empty(max(1, T - 1), K, F_out, F_out)) if in_channels > 0: self.init_weight = Parameter(torch.empty(K, F_in, F_out)) self.root_weight = Parameter(torch.empty(T, K, F_in, F_out)) else: self.init_weight = torch.nn.parameter.UninitializedParameter() self.root_weight = torch.nn.parameter.UninitializedParameter() self._hook = self.register_forward_pre_hook( self.initialize_parameters) if bias: self.bias = Parameter(torch.empty(T, K, 1, F_out)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() glorot(self.weight) if not isinstance(self.init_weight, torch.nn.UninitializedParameter): glorot(self.init_weight) glorot(self.root_weight) zeros(self.bias)
[docs] def forward(self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None) -> Tensor: if isinstance(edge_index, Tensor): edge_index, edge_weight = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), add_self_loops=False, flow=self.flow, dtype=x.dtype) elif isinstance(edge_index, SparseTensor): edge_index = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), add_self_loops=False, flow=self.flow, dtype=x.dtype) x = x.unsqueeze(-3) out = x for t in range(self.num_layers): if t == 0: out = out @ self.init_weight else: out = out @ self.weight[0 if self.shared_weights else t - 1] # propagate_type: (x: Tensor, edge_weight: OptTensor) out = self.propagate(edge_index, x=out, edge_weight=edge_weight) root = F.dropout(x, p=self.dropout, root = root @ self.root_weight[0 if self.shared_weights else t] out = out + root if self.bias is not None: out = out + self.bias[0 if self.shared_weights else t] if self.act is not None: out = self.act(out) return out.mean(dim=-3)
def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: return edge_weight.view(-1, 1) * x_j def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: return spmm(adj_t, x, reduce=self.aggr) @torch.no_grad() def initialize_parameters(self, module, input): if isinstance(self.init_weight, nn.parameter.UninitializedParameter): F_in, F_out = input[0].size(-1), self.out_channels T, K = self.weight.size(0) + 1, self.weight.size(1) self.init_weight.materialize((K, F_in, F_out)) self.root_weight.materialize((T, K, F_in, F_out)) glorot(self.init_weight) glorot(self.root_weight) module._hook.remove() delattr(module, '_hook') def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels}, num_stacks={self.num_stacks}, ' f'num_layers={self.num_layers})')