Source code for torchdrug.models.gin

from collections.abc import Sequence

import torch
from torch import nn

from torchdrug import core, layers
from torchdrug.core import Registry as R


[docs]@R.register("models.GIN") class GraphIsomorphismNetwork(nn.Module, core.Configurable): """ Graph Ismorphism Network proposed in `How Powerful are Graph Neural Networks?`_ .. _How Powerful are Graph Neural Networks?: https://arxiv.org/pdf/1810.00826.pdf Parameters: input_dim (int): input dimension hidden_dims (list of int): hidden dimensions edge_input_dim (int, optional): dimension of edge features num_mlp_layer (int, optional): number of MLP layers eps (int, optional): initial epsilon learn_eps (bool, optional): learn epsilon or not short_cut (bool, optional): use short cut or not batch_norm (bool, optional): apply batch normalization or not activation (str or function, optional): activation function concat_hidden (bool, optional): concat hidden representations from all layers as output readout (str, optional): readout function. Available functions are ``sum`` and ``mean``. """ def __init__(self, input_dim=None, hidden_dims=None, edge_input_dim=None, num_mlp_layer=2, eps=0, learn_eps=False, short_cut=False, batch_norm=False, activation="relu", concat_hidden=False, readout="sum"): super(GraphIsomorphismNetwork, self).__init__() if not isinstance(hidden_dims, Sequence): hidden_dims = [hidden_dims] self.input_dim = input_dim self.output_dim = hidden_dims[-1] * (len(hidden_dims) if concat_hidden else 1) self.dims = [input_dim] + list(hidden_dims) self.short_cut = short_cut self.concat_hidden = concat_hidden self.layers = nn.ModuleList() for i in range(len(self.dims) - 1): layer_hidden_dims = [self.dims[i + 1]] * (num_mlp_layer - 1) self.layers.append(layers.GraphIsomorphismConv(self.dims[i], self.dims[i + 1], edge_input_dim, layer_hidden_dims, eps, learn_eps, batch_norm, activation)) if readout == "sum": self.readout = layers.SumReadout() elif readout == "mean": self.readout = layers.MeanReadout() else: raise ValueError("Unknown readout `%s`" % readout)
[docs] def forward(self, graph, input, all_loss=None, metric=None): """ Compute the node representations and the graph representation(s). Parameters: graph (Graph): :math:`n` graph(s) input (Tensor): input node representations all_loss (Tensor, optional): if specified, add loss to this tensor metric (dict, optional): if specified, output metrics to this dict Returns: dict with ``node_feature`` and ``graph_feature`` fields: node representations of shape :math:`(|V|, d)`, graph representations of shape :math:`(n, d)` """ hiddens = [] layer_input = input for layer in self.layers: hidden = layer(graph, layer_input) if self.short_cut and hidden.shape == layer_input.shape: hidden = hidden + layer_input hiddens.append(hidden) layer_input = hidden if self.concat_hidden: node_feature = torch.cat(hiddens, dim=-1) else: node_feature = hiddens[-1] graph_feature = self.readout(graph, node_feature) return { "graph_feature": graph_feature, "node_feature": node_feature }