Source code for torch_geometric.nn.models.basic_gnn

from typing import Optional, Callable, List
from torch_geometric.typing import Adj

import copy

import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import ModuleList, Sequential, Linear, BatchNorm1d, ReLU

from torch_geometric.nn.conv import GCNConv, SAGEConv, GINConv, GATConv
from torch_geometric.nn.models.jumping_knowledge import JumpingKnowledge


class BasicGNN(torch.nn.Module):
    r"""An abstract class for implementing basic GNN models.

    Args:
        in_channels (int): Size of each input sample.
        hidden_channels (int): Size of each hidden sample.
        num_layers (int): Number of message passing layers.
        out_channels (int, optional): If not set to :obj:`None`, will apply a
            final linear transformation to convert hidden node embeddings to
            output size :obj:`out_channels`. (default: :obj:`None`)
        dropout (float, optional): Dropout probability. (default: :obj:`0.`)
        act (Callable, optional): The non-linear activation function to use.
            (default: :meth:`torch.nn.ReLU(inplace=True)`)
        norm (torch.nn.Module, optional): The normalization operator to use.
            (default: :obj:`None`)
        jk (str, optional): The Jumping Knowledge mode
            (:obj:`"last"`, :obj:`"cat"`, :obj:`"max"`, :obj:`"last"`).
            (default: :obj:`"last"`)
    """
    def __init__(self, in_channels: int, hidden_channels: int, num_layers: int,
                 out_channels: Optional[int] = None, dropout: float = 0.0,
                 act: Optional[Callable] = ReLU(inplace=True),
                 norm: Optional[torch.nn.Module] = None, jk: str = 'last'):
        super().__init__()
        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.num_layers = num_layers
        self.dropout = dropout
        self.act = act

        self.convs = ModuleList()

        self.norms = None
        if norm is not None:
            self.norms = ModuleList(
                [copy.deepcopy(norm) for _ in range(num_layers)])

        if jk != 'last':
            self.jk = JumpingKnowledge(jk, hidden_channels, num_layers)

        if out_channels is not None:
            self.out_channels = out_channels
            if jk == 'cat':
                self.lin = Linear(num_layers * hidden_channels, out_channels)
            else:
                self.lin = Linear(hidden_channels, out_channels)
        else:
            if jk == 'cat':
                self.out_channels = num_layers * hidden_channels
            else:
                self.out_channels = hidden_channels

    def reset_parameters(self):
        for conv in self.convs:
            conv.reset_parameters()
        for norm in self.norms or []:
            norm.reset_parameters()
        if hasattr(self, 'jk'):
            self.jk.reset_parameters()
        if hasattr(self, 'lin'):
            self.lin.reset_parameters()

    def forward(self, x: Tensor, edge_index: Adj, *args, **kwargs) -> Tensor:
        xs: List[Tensor] = []
        for i in range(self.num_layers):
            x = self.convs[i](x, edge_index, *args, **kwargs)
            if self.norms is not None:
                x = self.norms[i](x)
            if self.act is not None:
                x = self.act(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
            if hasattr(self, 'jk'):
                xs.append(x)

        x = self.jk(xs) if hasattr(self, 'jk') else x
        x = self.lin(x) if hasattr(self, 'lin') else x
        return x

    def __repr__(self) -> str:
        return (f'{self.__class__.__name__}({self.in_channels}, '
                f'{self.out_channels}, num_layers={self.num_layers})')


[docs]class GCN(BasicGNN): r"""The Graph Neural Network from the `"Semi-supervised Classification with Graph Convolutional Networks" <https://arxiv.org/abs/1609.02907>`_ paper, using the :class:`~torch_geometric.nn.conv.GCNConv` operator for message passing. Args: in_channels (int): Size of each input sample. hidden_channels (int): Size of each hidden sample. num_layers (int): Number of message passing layers. out_channels (int, optional): If not set to :obj:`None`, will apply a final linear transformation to convert hidden node embeddings to output size :obj:`out_channels`. (default: :obj:`None`) dropout (float, optional): Dropout probability. (default: :obj:`0.`) act (Callable, optional): The non-linear activation function to use. (default: :meth:`torch.nn.ReLU(inplace=True)`) norm (torch.nn.Module, optional): The normalization operator to use. (default: :obj:`None`) jk (str, optional): The Jumping Knowledge mode (:obj:`"last"`, :obj:`"cat"`, :obj:`"max"`). (default: :obj:`"last"`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.GCNConv`. """ def __init__(self, in_channels: int, hidden_channels: int, num_layers: int, out_channels: Optional[int] = None, dropout: float = 0.0, act: Optional[Callable] = ReLU(inplace=True), norm: Optional[torch.nn.Module] = None, jk: str = 'last', **kwargs): super().__init__(in_channels, hidden_channels, num_layers, out_channels, dropout, act, norm, jk) self.convs.append(GCNConv(in_channels, hidden_channels, **kwargs)) for _ in range(1, num_layers): self.convs.append( GCNConv(hidden_channels, hidden_channels, **kwargs))
[docs]class GraphSAGE(BasicGNN): r"""The Graph Neural Network from the `"Inductive Representation Learning on Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper, using the :class:`~torch_geometric.nn.SAGEConv` operator for message passing. Args: in_channels (int): Size of each input sample. hidden_channels (int): Size of each hidden sample. num_layers (int): Number of message passing layers. out_channels (int, optional): If not set to :obj:`None`, will apply a final linear transformation to convert hidden node embeddings to output size :obj:`out_channels`. (default: :obj:`None`) dropout (float, optional): Dropout probability. (default: :obj:`0.`) act (Callable, optional): The non-linear activation function to use. (default: :meth:`torch.nn.ReLU(inplace=True)`) norm (torch.nn.Module, optional): The normalization operator to use. (default: :obj:`None`) jk (str, optional): The Jumping Knowledge mode (:obj:`"last"`, :obj:`"cat"`, :obj:`"max"`). (default: :obj:`"last"`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.SAGEConv`. """ def __init__(self, in_channels: int, hidden_channels: int, num_layers: int, out_channels: Optional[int] = None, dropout: float = 0.0, act: Optional[Callable] = ReLU(inplace=True), norm: Optional[torch.nn.Module] = None, jk: str = 'last', **kwargs): super().__init__(in_channels, hidden_channels, num_layers, out_channels, dropout, act, norm, jk) self.convs.append(SAGEConv(in_channels, hidden_channels, **kwargs)) for _ in range(1, num_layers): self.convs.append( SAGEConv(hidden_channels, hidden_channels, **kwargs))
[docs]class GIN(BasicGNN): r"""The Graph Neural Network from the `"How Powerful are Graph Neural Networks?" <https://arxiv.org/abs/1810.00826>`_ paper, using the :class:`~torch_geometric.nn.GINConv` operator for message passing. Args: in_channels (int): Size of each input sample. hidden_channels (int): Size of each hidden sample. num_layers (int): Number of message passing layers. out_channels (int, optional): If not set to :obj:`None`, will apply a final linear transformation to convert hidden node embeddings to output size :obj:`out_channels`. (default: :obj:`None`) dropout (float, optional): Dropout probability. (default: :obj:`0.`) act (Callable, optional): The non-linear activation function to use. (default: :meth:`torch.nn.ReLU(inplace=True)`) norm (torch.nn.Module, optional): The normalization operator to use. (default: :obj:`None`) jk (str, optional): The Jumping Knowledge mode (:obj:`"last"`, :obj:`"cat"`, :obj:`"max"`). (default: :obj:`"last"`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.GINConv`. """ def __init__(self, in_channels: int, hidden_channels: int, num_layers: int, out_channels: Optional[int] = None, dropout: float = 0.0, act: Optional[Callable] = ReLU(inplace=True), norm: Optional[torch.nn.Module] = None, jk: str = 'last', **kwargs): super().__init__(in_channels, hidden_channels, num_layers, out_channels, dropout, act, norm, jk) self.convs.append( GINConv(GIN.MLP(in_channels, hidden_channels), **kwargs)) for _ in range(1, num_layers): self.convs.append( GINConv(GIN.MLP(hidden_channels, hidden_channels), **kwargs))
[docs] @staticmethod def MLP(in_channels: int, out_channels: int) -> torch.nn.Module: return Sequential( Linear(in_channels, out_channels), BatchNorm1d(out_channels), ReLU(inplace=True), Linear(out_channels, out_channels), )
[docs]class GAT(BasicGNN): r"""The Graph Neural Network from the `"Graph Attention Networks" <https://arxiv.org/abs/1710.10903>`_ paper, using the :class:`~torch_geometric.nn.GATConv` operator for message passing. Args: in_channels (int): Size of each input sample. hidden_channels (int): Size of each hidden sample. num_layers (int): Number of message passing layers. out_channels (int, optional): If not set to :obj:`None`, will apply a final linear transformation to convert hidden node embeddings to output size :obj:`out_channels`. (default: :obj:`None`) dropout (float, optional): Dropout probability. (default: :obj:`0.`) act (Callable, optional): The non-linear activation function to use. (default: :meth:`torch.nn.ReLU(inplace=True)`) norm (torch.nn.Module, optional): The normalization operator to use. (default: :obj:`None`) jk (str, optional): The Jumping Knowledge mode (:obj:`"last"`, :obj:`"cat"`, :obj:`"max"`). (default: :obj:`"last"`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.GATConv`. """ def __init__(self, in_channels: int, hidden_channels: int, num_layers: int, out_channels: Optional[int] = None, dropout: float = 0.0, act: Optional[Callable] = ReLU(inplace=True), norm: Optional[torch.nn.Module] = None, jk: str = 'last', **kwargs): super().__init__(in_channels, hidden_channels, num_layers, out_channels, dropout, act, norm, jk) if 'concat' in kwargs: del kwargs['concat'] if 'heads' in kwargs: assert hidden_channels % kwargs['heads'] == 0 out_channels = hidden_channels // kwargs.get('heads', 1) self.convs.append( GATConv(in_channels, out_channels, dropout=dropout, **kwargs)) for _ in range(1, num_layers): self.convs.append(GATConv(hidden_channels, out_channels, **kwargs))